Search results for: food composition data

Authors: Chih-Ping Chang

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Different from traditional objective evidence, social media evidence has its own characteristics with easily tampering, recoverability, and cannot be read without using other devices (such as a computer). Simply taking a screenshot from social network sites must be questioned its original identity. When the police search and seizure digital information, a common way they use is to directly print out digital data obtained and ask the signature of the parties at the presence, without taking original digital data back. In addition to the issue on its original identity, this conduct to obtain evidence may have another two results. First, it will easily allege that is tampering evidence because the police wanted to frame the suspect and falsified evidence. Second, it is not easy to discovery hidden information. The core evidence associated with crime may not appear in the contents of files. Through discovery the original file, data related to the file, such as the original producer, creation time, modification date, and even GPS location display can be revealed from hidden information. Therefore, how to show this kind of evidence in the courtroom will be arguably the most important task for ruling social media evidence. This article, first, will introduce forensic software, like EnCase, TCT, FTK, and analyze their function to prove the identity with another digital data. Then turning back to the court, the second part of this article will discuss legal standard for authentication of social media evidence and application of that forensic software in the courtroom. As the conclusion, this article will provide a rethinking, that is, what kind of authenticity is this rule of evidence chase for. Does legal system automatically operate the transcription of scientific knowledge? Or furthermore, it wants to better render justice, not only under scientific fact, but through multivariate debating.

Keywords: federal rule of evidence, internet forensic, printouts as evidence, social media evidence, United States v. Vayner

Procedia PDF Downloads 278

Authors: R. C. Ferreira, H. H. C. De Lima, A. A. Cândido, O. M. Couto Junior, P. A. Arroyo, K. Q De Carvalho, G. F. Gauze, M. A. S. D. Barros

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Removal of the widespread used drug paracetamol from water was investigated using activated carbon originated from dende coconut mesocarp and babassu coconut mesocarp. Kinetic and equilibrium data were obtained at different values of pH. Babassu activated carbon showed higher efficiency due to its acidity and higher microporosity. Pseudo-second order model was better adjusted to the kinetic results. Equilibrium data may be represented by Langmuir equation. Lower solution pH provided better removal efficiency as the carbonil groups may be attracted by the positively charged carbon surface.

Keywords: adsorption, activated carbon, babassu, dende

Procedia PDF Downloads 352

Authors: Udomporn Yingpaisuk, Premwadee Karuhadej

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The purposed of this research was to study the eating habit of teenage pregnancy and its relationship to the knowledge of nutrition during pregnancy. The 100 samples were derived from simple random sampling technique of the teenage pregnancy in Bangkae District. The questionnaire was used to collect data with the reliability of 0.8. The data were analyzed by SPSS for Windows with multiple regression technique. Percentage, mean and the relationship of knowledge of eating and eating behavior were obtained. The research results revealed that their knowledge in nutrition was at the average of 4.07 and their eating habit that they mentioned most was to refrain from alcohol and caffeine at 82% and the knowledge in nutrition influenced their eating habits at 54% with the statistically significant level of 0.001.

Keywords: teenage pregnancy, knowledge of eating, eating behavior, alcohol, caffeine

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Bayoumy Mohamed, Khaled Alam El-Din

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In the present study, 24 years of gridded sea level anomalies (SLA) from satellite altimetry and sea surface temperature (SST) from advanced very-high-resolution radiometer (AVHRR) daily data (1993-2016) are used. These data have been used to investigate the sea level rising and warming rates of SST, and their spatial distribution in the Mediterranean Sea. The results revealed that there is a significant sea level rise in the Mediterranean Sea of 2.86 ± 0.45 mm/year together with a significant warming of 0.037 ± 0.007 °C/year. The high spatial correlation between sea level and SST variations suggests that at least part of the sea level change reported during the period of study was due to heating of surface layers. This indicated that the steric effect had a significant influence on sea level change in the Mediterranean Sea.

Keywords: altimetry, AVHRR, Mediterranean Sea, sea level and SST changes, trend analysis

Procedia PDF Downloads 178

Authors: M. K. Mzuza, W. Zhang, L. S. Chapola, M. Tembo

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Shire River is the largest and longest river in Malawi emptying its water into the Zambezi River in Mozambique. Siltation is now a major problem in the Shire River due to catchment degradation. This study analysed soil samples from tributaries of the Shire River to determine sources of sediments that cause siltation using the mineral magnetic approach. Bulk sediments and separated particle size fractions of representative samples were collected from tributaries on the western and eastern sides of the Shire River, and Nkula Dam. Eastern tributaries showed relatively higher ferrimagnetic mineral contents and ferrimagnetic to anti ferromagnetic ratios than western tributaries. Sediments from both sides of the Shire River were distinguished by χARM, SIRM versus χlf and S-100 versus SIRM. Findings in this study showed that most of the sediments originated from the western part of the Shire River. Tributaries on the eastern side of the Shire River had higher values for concentration related parameters (χlf, χfd, χARM, SIRM, HIRM, S-100, and χARM/SIRM) than tributaries on the western side. Bulky and detailed magnetic measurements carried out on particle size fractions provided additional confirmation of magnetic contrasts between the two sides of the river suggesting differences in lithology, topography, climate and weather regimes in the catchments. This study demonstrated that the magnetic approach can provide a reliable means of understanding major sediment sources of Nkula Dam and similar situations. It can also help to assess future variations in sediment composition resulting from catchment changes

Keywords: ferrimagnetic minerals, Shire River, tributaries rivers, particle size , topography

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Authors: Ionela-Daniela Carja, Diana Serbezeanu, Tachita Vlad-Bubulac, Corneliu Hamciuc

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Nowadays, epoxy materials are extensively used in ever more areas and under ever more demanding environmental conditions due to their remarkable combination of properties, light weight and ease of processing. However, these materials greatly increase the fire risk due to their flammability and possible release of toxic by-products as a result of their chemical composition which consists mainly from carbon and hydrogen atoms. Therefore, improving the fire retardant behaviour to prevent the loss of life and property is of particular concern among government regulatory bodies, consumers and manufacturers alike. Modification of epoxy resins with organophosphorus compounds, as reactive flame retardants or additives, is the key to achieving non-flammable advanced epoxy materials. Herein, a detailed characterization of fire behaviour for a series of phosphorus-containing epoxy thermosets is reported. A carefully designed phosphorus flame retardant additive was simply blended with a bifunctional bisphenol-A based epoxy resin. Further thermal cross-linking in the presence of various aminic hardeners led to eco-friendly flame retardant epoxy resins. The type of hardener, concentration of flame retardant additive, compatibility between the components of the mixture, char formation and morphology, thermal stability, flame retardant mechanisms were investigated. It was found that even a very low content of phosphorus introduced into the epoxy matrix increased the limiting oxygen index value to about 30%. In addition, the peak of the heat release rate value decreased up to 45% as compared to the one of the neat epoxy system. The main flame retardant mechanism was the condensed-phase one as revealed by SEM and XPS measurements.

Keywords: condensed-phase mechanism, eco-friendly phosphorus flame retardant, epoxy resin, thermal stability

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Authors: Meity Sompie, Agnes Triasih, Wisje Ponto

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Edible coating is thin layers that act as a barrier to the external factors and protect the food products. The addition of the plasticizer to the edible coating is required to overcome film caused by extensive intermolecular forces. The potential development of pigskin with different ages as a raw material for the manufacture of edible films had not been widely publicized. This research was aimed to determine the influence of gelatin concentration and different type of plasticizer on the edible coating characteristics extracted from pigskin gelatin. This study used Completely Randomized Design (CRD) with two factors and three replicates of treatments. The first factor was consisted of pigskin gelatin concentration ( 10, 20, and 30 %) and the second factor was different type of plasticizer (glycerol, sorbitol and PEG). The results show that the interaction between the use of gelatin concentrations and type of plasticizer had significant effect (P< 0.05) on the thickness, tensile strength, elongation, water vapor transmission rate (WVTR), water content and amino acid profile of edible coating. It was concluded that the edible coating from pigskin gelatin with plasticizer gliserol had the best film characteristics, and it can be applied as an edible coating.

Keywords: edible coating, edible film, pigskin gelatin, plasticizer

Procedia PDF Downloads 191

Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Ju-Young Kim, Tae-Heon Moon, Jung-Hun Cho

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Urban regeneration projects have been actively promoted in Korea. In particular, Jeonju Hanok Village is evaluated as one of representative cases in terms of utilizing local cultural heritage sits in the urban regeneration project. However, recently, there has been a growing concern in this area, due to the ‘gentrification’, caused by the excessive commercialization and surging tourists. This trend was changing land and building use and resulted in the loss of identity of the region. In this regard, this study analyzed the land use transformation between 2010 and 2016 to identify the commercialization trend in Jeonju Hanok Village. In addition, it conducted SNS big data analysis on Jeonju Hanok Village from February 14th, 2016 to March 31st, 2016 to identify visitors’ awareness of the village. The study results demonstrate that rapid commercialization was underway, unlikely the initial intention, so that planners and officials in city government should reconsider the project direction and rebuild deliberate management strategies. This study is meaningful in that it analyzed the land use transformation and SNS big data to identify the current situation in urban regeneration area. Furthermore, it is expected that the study results will contribute to the vitalization of regeneration area.

Keywords: land use, SNS, text mining, urban regeneration

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Authors: Bahram Fathi, Mahdi Khodaparast Mashhadi, Masuod Homayounifar

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According to 1404 forecasting documentation, among the most fundamental ways of Iran’s success in competition with other regional countries are innovations, efficiency enhancements and domestic productivity. Therefore, in this study, the energy consumption efficiency of Iran and the neighbor countries has been measured in the period between 2007-2012 considering the simultaneous economic activities, CO2 emission, and consumption of energy through data envelopment analysis of undesirable output. The results of the study indicated that the energy efficiency changes in both Iran and the average neighbor countries has been on a descending trend and Iran’s energy efficiency status is not desirable compared to the other countries in the region.

Keywords: energy efficiency, environmental, undesirable output, data envelopment analysis

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Junita Batubara

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Children with special needs, especially those with disability in mental, physical or social/emotional interactions, are marginalized. Many people still view them as troublesome, inconvenience, having learning difficulties, unproductive and burdensome to society. This study intends to investigate; how musical drama can develop the ability to control the coordination of mental functions; how musical dramas can assist children to work together; how musical dramas can assist to maintain the child's emotional and physical health; how musical dramas can improve children creativity. The objectives of the research are: To know whether musical drama can control the coordination of mental function of children; to know whether musical drama can improve communication ability and expression of children; to know whether musical drama can help children work with people around them; to find out if musical dramas can develop the child's emotional and physical health; to find out if musical drama can improve children's creativity. The study employed a qualitative research approach. Data was collecting by listening, observing in depth through public hearings that select the key informants who were teachers and principals, parents and children. The data obtained from each public hearing was then processed (reduced), conclusion drawing/verification, presentation of data (data display). Furthermore, the model obtained was implementing for musical performance, where the benefits of the show are: musical drama can improve language skills; musical dramas are capable of developing memory and storage of information; developing communication skills and express themselves; helping children work together; assisting emotional and physical health; enhancing creativity.

Keywords: children Down syndrome, music, drama script, performance

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Authors: Daniela Rimnacova, Maryna Vorokhta, Martina Svabova

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CO₂ adsorption capacity of several materials - waste (power fly ash, slag, carbonized sewage sludge), rocks (Czech Silurian shale, black coal), and carbon (synthesized carbon, activated carbon as a reference material) - were measured on dry samples using a unique hand-made manometric sorption apparatus at a temperature of 45 °C and pressures of up to 7 MPa. The main aim was finding utilization of the waste materials and rocks for removal of the air or water pollutants caused by anthropogenic activities, as well as for the carbon dioxide storage. The equilibrium amount of the adsorbate depends on temperature, gas saturation pressure, porosity, surface area and volume of pores, and last but not least, on the composition of the adsorbents. Given experimental conditions can simulate in-situ situations in the rock bed and can be achieved just by a high-pressure apparatus. The CO₂ excess adsorption capacities ranged from 0.018 mmol/g (ash) to 13.55 mmol/g (synthesized carbon). The synthetized carbon had the highest adsorption capacity among all studied materials as well as the highest price. This material is usually used for the adsorption of specific pollutants. The excess adsorption capacity of activated carbon was 9.19 mmol/g. It is used for water and air cleaning. Ash can be used for chemisorption onto ash particle surfaces or capture of special pollutants. Shale is a potential material for enhanced gas recovery or CO₂ sequestration in-situ. Slag is a potential material for capture of gases with a possibility of the underground gas storage after the adsorption process. The carbonized sewage sludge is quite a good adsorbent for the removal and capture of pollutants, as well as shales or black coal which show an interesting relationship between the price and adsorption capacity.

Keywords: adsorption, CO₂, high pressure, porous materials

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Authors: Sudeepti Dayal, Neelesh Gupta

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In terms of transmission, bigger the size of any image, longer the time the channel takes for transmission. It is understood that the bandwidth of the channel is fixed. Therefore, if the size of an image is reduced, a larger number of data or images can be transmitted over the channel. Compression is the technique used to reduce the size of an image. In terms of storage, compression reduces the file size which it occupies on the disk. Any image is based on two parameters, region of interest and non-region of interest. There are several algorithms of compression that compress the data more economically. In this paper we have reviewed region of interest and non-region of interest based compression techniques and the algorithms which compress the image most efficiently.

Keywords: compression ratio, region of interest, DCT, DWT

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 95

Authors: Kiran Datta, Daniella Holland-Hart, Anthony Byrne

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Introduction: Oesophago-gastric (OG) cancers are the fifth commonest in the UK, accounting for over 12,000 deaths each year. Most patients will present at later stages of the disease, with only 21% of patients with stage 4 disease surviving longer than a year. As a result, many patients are unsuitable for curative surgery and instead receive palliative treatment to improve prognosis and symptom burden. However, palliative chemotherapy can result in significant toxicity: almost half of the patients are unable to complete their chemotherapy regimen, with this proportion rising significantly in older and frailer patients. In addition, clinical trials often exclude older and frailer patients due to strict inclusion criteria, meaning there is limited evidence to guide which patients are most likely to benefit from palliative chemotherapy. Inappropriate chemotherapy administration is at odds with the goals of palliative treatment and care, which are to improve quality of life, and this also represents a significant resource expenditure. This literature review aimed to examine and appraise evidence regarding treatment resilience in order to guide clinicians in identifying the most suitable candidates for palliative chemotherapy. Factors influencing treatment resilience were assessed, as measured by completion rates, dose reductions, and toxicities. Methods: This literature review was conducted using rapid review methodology, utilising modified systematic methods. A literature search was performed across the MEDLINE, EMBASE, and Cochrane Library databases, with results limited to papers within the last 15 years and available in English. Key inclusion criteria included: 1) participants with either oesophageal, gastro-oesophageal junction, or gastric cancers; 2) patients treated with palliative chemotherapy; 3) available data evaluating the association between baseline participant characteristics and treatment resilience. Results: Of the 2326 papers returned, 11 reports of 10 studies were included in this review after excluding duplicates and irrelevant papers. Treatment resilience factors that were assessed included: age, performance status, frailty, inflammatory markers, and sarcopenia. Age was generally a poor predictor for how well patients would tolerate chemotherapy, while poor performance status was a better indicator of the need for dose reduction and treatment non-completion. Frailty was assessed across one cohort using multiple screening tools and was an effective marker of the risk of toxicity and the requirement for dose reduction. Inflammatory markers included lymphopenia and the Glasgow Prognostic Score, which assessed inflammation and hypoalbuminaemia. Although quick to obtain and interpret, these findings appeared less reliable due to the inclusion of patients treated with palliative radiotherapy. Sarcopenia and body composition were often associated with chemotherapy toxicity but not the rate of regimen completion. Conclusion: This review demonstrates that there are numerous measures that can estimate the ability of patients with oesophago-gastric cancer to tolerate palliative chemotherapy, and these should be incorporated into clinical assessments to promote personalised decision-making around treatment. Age should not be a barrier to receiving chemotherapy and older and frailer patients should be included in future clinical trials to better represent typical patients with oesophago-gastric cancers. Decisions regarding palliative treatment should be guided by these factors identified as well as patient preference.

Keywords: frailty, oesophago-gastric cancer, palliative chemotherapy, treatment resilience

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Authors: Hamidreza Fazlollahi

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The Re ́nyi entropy comprises a group of data estimates that sums up the well-known Shannon entropy, acquiring a considerable lot of its properties. It appears as unqualified and restrictive entropy, relative entropy, or common data, and has found numerous applications in information theory. In the Re ́nyi’s argument, the area law of the black hole entropy plays a significant role. However, the total entropy can be modified by some quantum effects, motivated by the randomness of a system. In this note, by employing this modified entropy relation, we have derived corrections to Friedmann equations. Taking this entropy associated with the apparent horizon of the Friedmann-Robertson-Walker Universe and assuming the first law of thermodynamics, dE=T_A (dS)_A+WdV, satisfies the apparent horizon, we have reconsidered expanding Universe. Also, the second thermodynamics law has been examined.

Keywords: Friedmann equations, dark energy, first law of thermodynamics, Reyni entropy

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Authors: Arjun G. Koppad

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The study was conducted in Joida taluk of Uttara Kannada district, Karnataka, India, to assess the land use land cover (LULC) and forest aboveground biomass using L band SAR data. The study area covered has dense, moderately dense, and sparse forests. The sampled area was 0.01 percent of the forest area with 30 sampling plots which were selected randomly. The point center quadrate (PCQ) method was used to select the tree and collected the tree growth parameters viz., tree height, diameter at breast height (DBH), and diameter at the tree base. The tree crown density was measured with a densitometer. Each sample plot biomass was estimated using the standard formula. In this study, the LULC classification was done using Freeman-Durden, Yamaghuchi and Pauli polarimetric decompositions. It was observed that the Freeman-Durden decomposition showed better LULC classification with an accuracy of 88 percent. An attempt was made to estimate the aboveground biomass using SAR backscatter. The ALOS-2 PALSAR-2 L-band data (HH, HV, VV &VH) fully polarimetric quad-pol SAR data was used. SAR backscatter-based regression model was implemented to retrieve forest aboveground biomass of the study area. Cross-polarization (HV) has shown a good correlation with forest above-ground biomass. The Multi Linear Regression analysis was done to estimate aboveground biomass of the natural forest areas of the Joida taluk. The different polarizations (HH &HV, VV &HH, HV & VH, VV&VH) combination of HH and HV polarization shows a good correlation with field and predicted biomass. The RMSE and value for HH & HV and HH & VV were 78 t/ha and 0.861, 81 t/ha and 0.853, respectively. Hence the model can be recommended for estimating AGB for the dense, moderately dense, and sparse forest.

Keywords: forest, biomass, LULC, back scatter, SAR, regression

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Authors: Olga S. Volodko, Lidiya A. Kompaniets, Ludmila V. Gavrilova

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The method of empirical orthogonal functions is the method of data analysis with a complex spatial-temporal structure. This method allows us to decompose the data into a finite number of modes determined by empirically finding the eigenfunctions of data correlation matrix. The modes have different scales and can be associated with various physical processes. The empirical orthogonal function method has been widely used for the analysis of hydrophysical characteristics, for example, the analysis of sea surface temperatures in the Western North Atlantic, ocean surface currents in the North Carolina, the study of tropical wave disturbances etc. The method used in this study has been applied to the analysis of temperature and velocity measurements in saline Lake Shira (Southern Siberia, Russia). Shira is a shallow lake with the maximum depth of 25 m. The lake Shira can be considered as a closed water site because of it has one small river providing inflow and but it has no outflows. The main factor that causes the motion of fluid is variable wind flows. In summer the lake is strongly stratified by temperature and saline. Long-term measurements of the temperatures and currents were conducted at several points during summer 2014-2015. The temperature has been measured with an accuracy of 0.1 ºC. The data were analyzed using the empirical orthogonal function method in the real version. The first empirical eigenmode accounts for 70-80 % of the energy and can be interpreted as temperature distribution with a thermocline. A thermocline is a thermal layer where the temperature decreases rapidly from the mixed upper layer of the lake to much colder deep water. The higher order modes can be interpreted as oscillations induced by internal waves. The currents measurements were recorded using Acoustic Doppler Current Profilers 600 kHz and 1200 kHz. The data were analyzed using the empirical orthogonal function method in the complex version. The first empirical eigenmode accounts for about 40 % of the energy and corresponds to the Ekman spiral occurring in the case of a stationary homogeneous fluid. Other modes describe the effects associated with the stratification of fluids. The second and next empirical eigenmodes were associated with dynamical modes. These modes were obtained for a simplified model of inhomogeneous three-level fluid at a water site with a flat bottom.

Keywords: Ekman spiral, empirical orthogonal functions, data analysis, stratified fluid, thermocline

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Authors: Jifang Tao, Fei Gao, Hong Cai, Yuan Jin Zheng, Yuan Dong Gu

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Photoacoustic technology is used to measure effect absorption of a light by means of acoustic detection, which provides a high sensitive, low-cross response, cost-effective solution for gas molecular detection. In this paper, we proposed an integrated photoacoustic sensor for Indoor-air quality (IAQ) monitoring. The sensor consists of an acoustically resonant cavity, a high silicon acoustic transducer chip, and a low-cost light source. The light is modulated at the resonant frequency of the cavity to create an enhanced periodic heating and result in an amplified acoustic pressure wave. The pressure is readout by a novel acoustic transducer with low noise. Based on this photoacoustic sensor, typical indoor gases, including CO2, CO, O2, and H2O have been successfully detected, and their concentration are also evaluated with very high accuracy. It has wide potential applications in IAQ monitoring for agriculture, food industry, and ventilation control systems used in public places, such as schools, hospitals and airports.

Keywords: indoor-air quality (IAQ) monitoring, photoacoustic gas sensor, cavity enhancement, integrated gas sensor

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Authors: George Ogono Muok, N. Obange, S. A. Odhiambo

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Empirical literature on the determinants of foreign direct investments (FDI) flows is extensive but controversial over some determinants of FDI in-flows in developing countries. The objective of this study therefore was to investigate the overall determinants of FDI inflows in Kenya. Dynamic macroeconomic theory and correlational study design provided theoretical framework for specification of a time series model. The study used data observed from 1970 to 2015 in World Development Indicators (WDI) data bank. The results show that annual growth rate of GDP, inflation rates and external debt as a proportion of GDP are significant determinants of FDI inflows in Kenya and are therefore important macroeconomic parameters for policy formulation for promotion of FDI inflows in Kenya.

Keywords: determinants of foreign, direct, investment inflows in, Kenya, Africa

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Authors: Zelalem Fantahun

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Part-of-speech tagging is the process of assigning a part-of-speech or other lexical class marker to each word into naturally occurring text. Part-of-speech tagging is the most fundamental and basic task almost in all natural language processing. In natural language processing, the problem of providing large amount of manually annotated data is a knowledge acquisition bottleneck. Since, Amharic is one of under-resourced language, the availability of tagged corpus is the bottleneck problem for natural language processing especially for POS tagging. A promising direction to tackle this problem is to provide a system that does not require manually tagged data. In unsupervised learning, the learner is not provided with classifications. Unsupervised algorithms seek out similarity between pieces of data in order to determine whether they can be characterized as forming a group. This paper explicates the development of unsupervised part-of-speech tagger using K-Means clustering for Amharic language since large amount of data is produced in day-to-day activities. In the development of the tagger, the following procedures are followed. First, the unlabeled data (raw text) is divided into 10 folds and tokenization phase takes place; at this level, the raw text is chunked at sentence level and then into words. The second phase is feature extraction which includes word frequency, syntactic and morphological features of a word. The third phase is clustering. Among different clustering algorithms, K-means is selected and implemented in this study that brings group of similar words together. The fourth phase is mapping, which deals with looking at each cluster carefully and the most common tag is assigned to a group. This study finds out two features that are capable of distinguishing one part-of-speech from others these are morphological feature and positional information and show that it is possible to use unsupervised learning for Amharic POS tagging. In order to increase performance of the unsupervised part-of-speech tagger, there is a need to incorporate other features that are not included in this study, such as semantic related information. Finally, based on experimental result, the performance of the system achieves a maximum of 81% accuracy.

Keywords: POS tagging, Amharic, unsupervised learning, k-means

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Authors: Nasim Rasuli, Akram Ahmadi, Hossein Riahi, Zeinab Shariatmadari, Majid Ghorbani Nohooji, Pooyan Mehraban Joubani

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Cyanobacteria are one of the most promising sources of new biostimulants and have received much attention due to their diverse applications in biotechnology. These microorganisms enhance the growth and productivity of plants by producing plant growth stimulants and fixing atmospheric nitrogen. Thymus vulgaris L., a valuable medicinal plant from the Lamiaceae family, is widely distributed across the globe. essential oil of T. vulgaris is best characterized by the prominence of phenols, making them the key compounds in its composition. Lignin biosynthesis as a natural plant polyphenol plays a crucial role in promoting plant growth, strengthening cell walls, and increasing resistance to pathogens. In this study, the bioelicitor activity of five cyanobacterial suspensions including Anabaena torulosa ISB213, Nostoc calcicola ISB215, Nostoc ellipsosporum ISB217, Trichormus doliolum ISB214, and Oscillatoria sp. ISB2116 on the lignin content of the T. vulgaris L. was investigated. Pot experiments were performed by inoculation of a %2 algal extract to the soil of treated plants one week before planting and then every 20 days. After four months, the lignin content in the leaves of both treated and control plants was evaluated. The results demonstrated that the application of cyanobacteria significantly increased the lignin content in the leaves of treated plants compared to the control. The treatment with Oscillatoria sp. ISB216 and N. ellipsosporum ISB217 resulted in the highest lignin content, with an increase of 93.33% and 86.67%, respectively. These findings highlight the potential of cyanobacteria as bioelicitors, offering a viable alternative for enhancing the production of secondary metabolites in T. vulgaris. Consequently, this could contribute to the economic value of this medicinal plant.

Keywords: cyanobacteria, bioelicitor, thymus vulgaris, lignin

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Authors: Sayyed Abdolmajid Jalaee Esfand Abadi, Sepideh Samimi

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Since one of the major difficulties to develop small manufacturing enterpriser in developing countries is the limitations of financing activities, this paper want to answer the question: “what is the role and status of micro finance in improving the labor productivity of small industries in Iran?” The results of panel data estimation show that micro finance in Iran has not yet been able to work efficiently and provide the required credit and investment. Also, reducing economy’s dependence on oil revenues reduced and increasing its reliance on domestic production and exports of industrial production can increase the productivity of workforce in Iranian small industries.

Keywords: microfinance, small manufacturing enterprises (SME), workforce productivity, Iran, panel data

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Authors: So Yae Koh, YeonWoo Song, Ji-Yeon Ryu, Jeong Yong Moon, Somi Kim Cho

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Blueberry, a perennial shrub, is one of the most popular fruits due to its flavor and strong free radical scavenging properties. In this study, the blueberry puree was fermented by Lactobacillus plantarum CK10 and the antioxidant activities of fermentation products were examined. Various conditions with different supplements (5% sucrose or 10% skim milk) were evaluated for fermentation efficiency and the effects on antioxidant properties. The viable cell count of lactic acid bacteria, pH, total phenolic compounds and flavonoids contents were measured after 7 days of fermentation. DPPH (1,1-diphenyl-2-picrylhydrazyl) and ABTS [2,2’-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid)] radical scavenging activities were highly enhanced compared to non-fermented blueberry puree after fermentation. Interestingly, the antioxidant activities were greatly increased in the fermentation of blueberry puree alone without supplements. The present results indicate that the blueberry puree fermented by Lactobacillus plantarum CK10 could be used as a potential source of natural antioxidants and these findings will facilitate the utilization of blueberry as a resource for food additive.

Keywords: antioxidant activity, blueberry, lactobacillus plantarum CK10, fermentation

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Authors: V. Yu. Volgutov, A. I. Orlova, S. A. Khainakov

Abstract:

NaZr2(PO4)3 (NZP) and their structural analogues are characterized by a peculiar behaviors on heating – they have different expansion and contraction along different crystallographic directions due to specific arrangements of crystal structure in these compounds. An important feature of such structures is the ability to incorporate into their structural analogues wide variety of metal cations having different size and oxidation states, with different combinations and concentrations. These cations are located in different crystallographic non-equivalent positions of octahedral tetrahedral crystal framework as well as in inter-framework cavities. Through, due to iso- and hetero-valent isomorphism of the cations (and the anions) in NZP, it becomes possible to tuning the compositions and to obtain the compounds with ‘on a plan’ properties. For the design of compounds with low and ultra-low thermal expansion including those with tailored thermal expansion properties, the following crystallochemical principles it seems are promising: 1) Insertion into crystal M1 position the cations having different sizes and, 2) the variation in the composition of compounds, providing different occupation of crystal M1 position. Following these principles we have designed and synthesized the next NZP-type phosphates series: a) where radii of the cations in the M1 crystal position was varied: Zr1/4Zr2(PO4)3 - Th1/4Zr2(PO4)3 (series I); R1/3Zr2(PO4)3 where R= Nd, Eu, Er (series II), b) where the occupation of M1 crystal position was varied: Zr1/4Zr2(PO4)3-Er1/3Zr2(PO4)3 (series III) and Zr1/4Zr2(PO4)3-Sr1/2Zr2(PO4)3 (series IV). The thermal expansion parameters were determined over the range of 25-800ºC. For each series the minimum axial coefficient of thermal expansion αa = αb, αc and their anisotropy Δα = Iαa - αcI, 10-6 K-1 was found as next: -1.51, 1.07, 2.58 for Th1/4Zr2(PO4)3 (series I); -0.72, 0.10, 0.81 for Nd1/3Zr2(PO4)3 (series II); -2.78, 1.35, 4.12 for Er1/6Zr1/8Zr2(PO4)3 (series III); 2.23, 1.32, 0.91 for Sr1/2Zr2(PO4)3 (series IV). The measured tendencies of the thermal expansion of crystals were in good agreement with predicted ones. For one of the members from the studied phosphates namely Th1/16Zr3/16Zr2(PO4)3 structural refinement have been carried out at 25, 200, 600, and 800°C. The dependencies of the structural parameters with the temperature have been determined.

Keywords: high-temperature crystallography, NaZr2(PO4)3, (NZP) analogs, structural-chemical principles, tuning thermal expansion

Procedia PDF Downloads 223

Authors: Najat El-Kurdi, Sherif Hammad, Mohamed Ghazi, Sahar El-Shatoury, Khaled Zakaria

Abstract:

The increasing number of plastic production and its importance to humanity in daily life made it a headache to the planet earth. The persistence of plastic wastes in the environment formed a serious problem. They are prominent with their capability to resist microbial degradation for decades. Thus, it was crucial to find ways to eliminate the plastics without depending on conventional recycling methods, which causes the formation of more hazardous compounds and doubles the problem. In this paper, mealworms were fed with a mixture of plastic wastes such as plastic bags, Styrofoam, PE foam, and plastic tarpaulins film as the sole food source for a month. Frass was collected at the end of the test and examined using FTIR analysis. Also, the gut bacteria were isolated and identified using 16S rRNA. The results show the mineralization of plastic in the frass of plastic-fed worms when compared to control. The 16S rRNA and the BLAST analysis showed that the obtained isolate belongs to the genus Bacillus Sp especially Bacillus subtilis. Phylogenetic analysis showed their relatedness to the other Bacillus species in the NCBI database.

Keywords: mealworm, waste management, plastic-degrading bacteria, gut microbiome, Bacillus sp

Procedia PDF Downloads 132

Authors: Flavio Cannavo

Abstract:

Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

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Authors: Ahmed Emara, Maged Ghoneima, Mohamed Dessouky

Abstract:

Demand for low power fast memories is increasing with the increase in IC’s complexity, in this paper we introduce a proposal for a compact SRAM based on memristor devices. The compact size of the proposed cell (1T2M compared to 6T of traditional SRAMs) allows denser memories on the same area. In this paper, we will discuss the proposed memristor memory cell for single/multi bit data storing configurations along with the writing and reading operations. Stored data stability across successive read operation will be illustrated, operational simulation results and a comparison of our proposed design with previously conventional SRAM and previously proposed memristor cells will be provided.

Keywords: memristor, multi-bit, single-bit, circuits, systems

Procedia PDF Downloads 357