Search results for: binary segmentation

Authors: Ajay Kumar

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Using data from census of India, sample registration system and national family health survey (NFHS-3), this paper traces spatial pattern, trends and the factors which have played their role differently in fertility transition in Uttar Pradesh and Tamil Nadu. For the purpose spatial variation analysis, trend line and binary logistic regression analysis has been carried out. There exist considerable regional disparities in terms of fertility decline in northern and southern states. The pace of fertility decline has been faster in southern and coastal regions, and at a slow pace in backward northern state. In Tamil Nadu fertility declined substantially among the women of lower and higher age groups in comparison to Uttar Pradesh characterized by low literacy, low female age at marriage, poor health infrastructure and low status of women. The Study shows that Fertility rates have been higher among the most vulnerable and deprived sections of the society like Illiterate women, women belong to scheduled caste, scheduled tribe and women residing in rural areas.

Keywords: age specific fertility rate, fertility transition, replacement level, total fertility rate

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Authors: Tripta Dixit, Smita Sahu, William Selvamurthy, Sadhana Srivastava

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India is battling with the issues of accessibility, affordability and availability of quality health to the masses. Indian medical heritage which dated back to 3000 BC unveils the rich knowledge pool which has undergone a perceptible change over years, such as eradication of many communicable diseases, increasing individual awareness of quality health and import driven medical device market etc. Despite a slew of initiatives the holistic slogan of ‘health for all’ remains elusive and a concern for the nation. The 21st-century projects a myriad of challenges like cultural diversity, large population, demographic dividend and geographical segmentation leading to varied needs of people as per their regional conditions of climate, disease prevalence, nutrition and sanitation. But these challenges are also opportunities for the development of indigenous, low cost and accessible technologies to tackle them. This requires reinforcing the potential of indigenous technologies in coordination with prevailing health issues in various regions of country. This paper emphasis on the strategy for exploring the indigenous technologies with entrusted up-scaling to meet the diverse needs of the people. This review proposes to adopt technology transfer as a strategy to establish a vibrant ecosystem for identifying and up-scaling the indigenous medical technologies with diligent hand-holding for public health.

Keywords: health, indigenous, medical technology, technology transfer

Procedia PDF Downloads 234

Authors: K. K. Soundra Pandian, Bhupendra Gupta

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In the modern era, as the application data’s are massive and complex, it needs to be secured from the adversary attack. In this context, a non-recursive key based integrated spritz stream cipher with the circulant hash function using delayed feedback with carry shift register (d-FCSR) is proposed in this paper. The novelty of this proposed stream cipher algorithm is to engender the improved keystream using d-FCSR. The proposed algorithm is coded using Verilog HDL to produce dynamic binary key stream and implemented on commercially available FPGA device Virtex 5 xc5vlx110t-2ff1136. The implementation of stream cipher using d-FCSR on the FPGA device operates at a maximum frequency of 60.62 MHz. It achieved the data throughput of 492 Mbps and improved in terms of efficiency (throughput/area) compared to existing techniques. This paper also briefs the cryptanalysis of proposed circulant hash value based spritz stream cipher using d-FCSR is against the adversary attack on a hardware platform for the hardware based cryptography applications.

Keywords: cryptography, circulant function, field programmable gated array, hash value, spritz stream cipher

Procedia PDF Downloads 231

Authors: P. Ajmal Koya, Tariq Ahmad Wagay, K. Ismail

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One of the fundamental property of surfactant molecules are their ability to aggregate in water or binary mixtures of water and organic solvents as an effort to minimize their unfavourable interaction with the medium. In this work, influence two co-solvents (ethylene glycol (EG) and propylene glycol (PG)) on the aggregation properties of a cationic gemini surfactant, butanediyl-1,4-bis(tetradecyldimethylammonium bromide) (14–4–14), has been studied by conductance and steady state fluorescence at 298 K. The weight percentage of two co-solvents varied in between 0 and 50 % at an interval of 5 % up to 20 % and then 10 % up to 50 %. It was found that micellization process is delayed by the inclusion of both the co-solvents; consequently, a progressive increase was observed in critical micelle concentration (cmc) and Gibbs free energy of micellization (∆G0m), whereas a rough increase was observed in the values of degree of counter ion dissociation (α) and a decrease was obtained in values of average aggregation number (Nagg) and Stern-Volmer constant (KSV). At low weight percentage (up to 15 %) of co-solvents, 14–4–14 geminis were found to be almost equally prone to micellization both in EG–water (EG–WR) and in PG–water (PG–WR) mixed media while at high weight percentages they are more prone to micellization in EG–WR than in PG–WR mixed media.

Keywords: aggregation number, gemini surfactant, micellization, non aqueous solvent

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Najwa Mimouni, Omar Rahli, Rachid Bennacer, Salah Chikh

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In the present work 2D and 3D numerical simulations of double diffusion natural convection in an elongated enclosure filled with a binary fluid saturating a porous medium are carried out. In the formulation of the problem, the Boussinesq approximation is considered and cross Neumann boundary conditions are specified for heat and mass walls conditions. The numerical method is based on the control volume approach with the third order QUICK scheme. Full approximation storage (FAS) with full multigrid (FMG) method is used to solve the problem. For the explored large range of the controlling parameters, we clearly evidenced that the increase in the depth of the cavity i.e. the lateral aspect ratio has an important effect on the flow patterns. The 2D perfect parallel flows obtained for a small lateral aspect ratio are drastically destabilized by increasing the cavity lateral dimension. This yields a 3D fluid motion with a much more complicated flow pattern and the classically studied 2D parallel flows are impossible.

Keywords: bifurcation, natural convection, heat and mass transfer, parallel flow, porous media

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Authors: Ildar Akhmadullin, Mayank Tyagi

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The subject of this paper is the design analysis of a single well power production unit from low enthalpy geothermal resources. A complexity of the project is defined by a low temperature heat source that usually makes such projects economically disadvantageous using the conventional binary power plant approach. A proposed new compact design is numerically analyzed. This paper describes a thermodynamic analysis, a working fluid choice, downhole heat exchanger (DHE) and turbine calculation results. The unit is able to produce 321 kW of electric power from a low enthalpy underground heat source utilizing n-Pentane as a working fluid. A geo-pressured reservoir located in Vermilion Parish, Louisiana, USA is selected as a prototype for the field application. With a brine temperature of 126℃, the optimal length of DHE is determined as 304.8 m (1000ft). All units (pipes, turbine, and pumps) are chosen from commercially available parts to bring this project closer to the industry requirements. Numerical calculations are based on petroleum industry standards. The project is sponsored by the Department of Energy of the US.

Keywords: downhole heat exchangers, geothermal power generation, organic rankine cycle, refrigerants, working fluids

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Shih-Fang Lo, Tzu-Wei Guo, Chih-Hsuan Lee

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Tropical fisheries are vulnerable to the physical and biogeochemical oceanic changes associated with climate change. Warmer temperatures and extreme weather have beendamaging the abundance and growth patterns of aquatic organisms. In recent year, the shrinking of fish stock and labor shortage have increased the threat to global aquacultural production. Thus, building a climate-resilient and sustainable mechanism becomes an urgent, important task for global citizens. To tackle the problem, Taiwanese fishermen applies the artificial intelligence (AI) technology. In brief, the AI system (1) measures real-time water quality and chemical parameters infish ponds; (2) monitors fish stock through segmentation, detection, and classification; and (3) implements fishermen’sprevious experiences, perceptions, and real-life practices. Applying this system can stabilize the aquacultural production and potentially increase the labor force. Furthermore, this AI technology can build up a more resilient and sustainable system for the fishermen so that they can mitigate the influence of extreme weather while maintaining or even increasing their aquacultural production. In the future, when the AI system collected and analyzed more and more data, it can be applied to different regions of the world or even adapt to the future technological or societal changes, continuously providing the most relevant and useful information for fishermen in the world.

Keywords: aquaculture, artificial intelligence (AI), real-time system, sustainable fishery

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Authors: Mengyue Gong, Xinyi Li, Amarjeet Bassi

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Harvesting by-products while recovering biodiesel is considered a potentially valuable approach to increase the market feasibility of microalgae industry. Lutein is a possible by-product from microalgae that promotes eye health. The extraction efficiency and the expensive drying process of wet algae represent the major challenges for the utilization of microalgae biomass as a feedstock for lipids, proteins, and carotenoids. A wet extraction method was developed to extract lipids and lutein from microalga Chlorella vulgaris. To evaluate different solvent (mixtures) for the extraction, a quantitative analysis was established based on the polarity of solvents using Nile Red as the polarity (ETN) indicator. By the choice of binary solvent system then adding proper amount of water to achieve phase separation, lipids and lutein can be extracted simultaneously. Some other parameters for lipids and lutein production were also studied including saponification time, temperature, choice of alkali, and pre-treatment methods. The extraction efficiency with wet algae was compared with dried algae and shown better pigment recovery. The results indicated that the product pattern in each extracted phase was polarity dependent. Lutein and β-carotene were the main carotenoids extracted with ethanol while lipids come out with hexane.

Keywords: biodiesel, Chlorella vulgaris, extraction, lutein

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Authors: B. Kabane, G. G. Redhi

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An ammonium based ionic liquid (methyltrioctylammonium chloride) [N_{8 8 8 1}] [Cl] was investigated as an extraction potential solvent for volatile organic solvents (in this regard, solutes), which includes alkenes, alkanes, ketones, alkynes, aromatic hydrocarbons, tetrahydrofuran (THF), alcohols, thiophene, water and acetonitrile based on the experimental activity coefficients at infinite THF measurements were conducted by the use of gas-liquid chromatography at four different temperatures (313.15 to 343.15) K. Experimental data of activity coefficients obtained across the examined temperatures were used in order to calculate the physicochemical properties at infinite dilution such as partial molar excess enthalpy, Gibbs free energy and entropy term. Capacity and selectivity data for selected petrochemical extraction problems (heptane/thiophene, heptane/benzene, cyclohaxane/cyclohexene, hexane/toluene, hexane/hexene) were computed from activity coefficients data and compared to the literature values with other ionic liquids. Evaluation of activity coefficients at infinite dilution expands the knowledge and provides a good understanding related to the interactions between the ionic liquid and the investigated compounds.

Keywords: separation, activity coefficients, methyltrioctylammonium chloride, ionic liquid, capacity

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Authors: Maryam Kiani

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Geopolymer has gained significant prominence worldwide and is now widely regarded as a potential alternative to conventional Portland cement. Nevertheless, for it to be widely accepted and incorporated into national and international standards, it is crucial to establish precise definitions and dependable mix design methodologies for geopolymer materials. The lack of a common definition and methodology has led to inconsistencies and perplexity across various areas of research. Addressing this concern is imperative for several reasons. To overcome the existing inconsistencies and confusion, concerted efforts should be made to establish clear definitions and robust mix design methodologies for geopolymer materials. This can be achieved through collaborative research, knowledge sharing, and engagement with industry experts. By doing so, we can pave the way for the widespread acceptance and utilization of geopolymer materials, revolutionizing the construction and infrastructure industry in a sustainable and environmentally friendly manner. The primary goal of this article is to offer clear explanations regarding the different meanings of geopolymer and the various methodologies used in geopolymer processes. Its main aim is to improve comprehension of both unary and binary geopolymer systems. By thoroughly exploring existing research, this article strives to illuminate the diverse methods and techniques utilized in the exciting field of geopolymer science.

Keywords: geopolymer, nanomaterials, structural materials, mechanical properties

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Authors: Milica Z. Karadzic, Lidija R. Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Z. Kovacevic, Anamarija I. Mandic, Katarina Penov-Gasi, Andrea R. Nikolic, Aleksandar M. Okljesa

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In this study, a set of 29 newly synthesized steroid derivatives were investigated using reversed-phase high-performance liquid chromatography (RP-HPLC) as a first step in preselection of drug candidates. This analysis presents an experimental determination of chromatographic lipophilicity, and it was conducted to obtain physicochemical characterization of these molecules. As the most widely used bonded phases in RP-HPLC, octadecyl (C18) and octyl (C8) were used. Binary mixtures of water and acetonitrile or methanol were used as mobile phases. Obtained results were expressed as retention factor values logk and they were correlated with logP values. The results showed that both columns provide good estimations of the chromatographic lipophilicity of the molecules included in this study. This analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their antiproliferative and antimicrobial activity. This article is based upon work from COST Action (CM1306), supported by COST (European Cooperation in Science and Technology).

Keywords: antiproliferative activity, chromatographic lipophilicity, liquid chromatography, steroids

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Authors: Anmar Dulaimi, Hassan Al Nageim, Felicite Ruddock, Linda Seton

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Fast-curing cold bituminous emulsion mixture (CBEM) including active filler from high calcium fly ash (HCFA) and waste material (LJMU-A2) has been developed in this study. This will overcome the difficulties related with the use of hot mix asphalt such as greenhouse gases emissions and problems in keeping the temperature when transporting long distance. The aim of this study is to employ petrographic examinations using scanning electron microscopy (SEM) for characterizing the hydrates microstructure, in a new binary blended cement filler (BBCF) system. The new BBCF has been used as a replacement to traditional mineral filler in cold bituminous emulsion mixtures (CBEMs), comprises supplementary cementitious materials containing high calcium fly ash (HCFA) and a waste material (LJMU-A2). SEM analysis demonstrated the formation of hydrates after varying curing ages within the BBCF. The accelerated activation of HCFA by LJMU-A2 within the BBCF was revealed and as a consequence early and later stiffness was developed in novel CBEM.

Keywords: cold bituminous emulsion mixtures, indirect tensile stiffness modulus, scanning electron microscopy (SEM), and high calcium fly ash

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Nihad Karim Chowdhury, Mohammad Sanaullah Chowdhury, Muhammed Jamshed Alam Patwary, Rubel Biswas

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In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset.

Keywords: 3D shape retrieval, 3D shape descriptor, CS-LBP, overlapped sub-images

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Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

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Authors: Eunseop Yeom

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Platelets can be activated by the surrounding blood flows where a blood vessel is narrowed as a result of atherosclerosis. Numerous studies have been conducted to identify the relation between platelets activation and thrombus formation. To measure platelet adhesion, this study proposes an image analysis technique. Blood samples are delivered in the microfluidic channel, and then platelets are activated by a stenotic micro-channel with 90% severity. By applying proposed correlation mapping, which visualizes decorrelation of the streaming blood flow, the area of adhered platelets (APlatelet) was estimated without labeling platelets. In order to evaluate the performance of correlation mapping on the detection of platelet adhesion, the effect of tile size was investigated by calculating 2D correlation coefficients with binary images obtained by manual labeling and the correlation mapping method with different sizes of the square tile ranging from 3 to 50 pixels. The maximum 2D correlation coefficient is observed with the optimum tile size of 5×5 pixels. As the area of the platelet adhesion increases, the platelets plug the channel and there is only a small amount of blood flows. This image analysis could provide new insights for better understanding of the interactions between platelet aggregation and blood flows in various physiological conditions.

Keywords: platelet activation, correlation coefficient, image analysis, shear rate

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Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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Authors: Tismazammi Mustafa, Jaharudin Padli

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The frequency of terrorism is increasing throughout years that is resulting in loss of life, damaging people’s property, and destructing the environment. The incident of terrorism is not stationed in one particular country but has spread and scattered in other countries hence causing an increase in the number of terrorism cases. Thus, this paper aims to investigate the factors of human development upon the terrorism in East Asia and Pacific countries. This study used a panel ARDL model, in which it enables to capture the long run and the short run relationship among the variables of interest. Logit Model for Binary data is also used, in which to representing an attributes of dependent variables. This study focuses on several human development variables namely GDP per capita, population, human capital, land area, and technologies. The empirical finding revealed that the GDP per capita, population, human capital, land area, and technologies are positively and statistically significant in influencing the terrorism. Thus, the finding in this study will present as grounds to preserve human rights and develop public awareness and will offer guidelines to policy makers, emergency managers, first responders, public health workers, physicians, and other researchers.

Keywords: terrorism, East Asia and Pacific, human development, econometric analysis

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Authors: Farzad Khajavi

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Recognition of the interaction between active pharmaceutical ingredients (API) and excipients is a pivotal factor in the development of all pharmaceutical dosage forms. By predicting the interaction between API and excipients, we will be able to prevent the advent of impurities or at least lessen their amount. In this study, we used principle component analysis (PCA) to predict the interaction between Gliclazide as a secondary amine with Lactose in pharmaceutical solid dosage forms. The infrared spectra of binary mixtures of Gliclazide with Lactose at different mole ratios were recorded, and the obtained matrix was analyzed with PCA. By plotting score columns of the analyzed matrix, the incompatibility between Gliclazide and Lactose was observed. This incompatibility was seen experimentally. We observed the appearance of the impurity originated from the Maillard reaction between Gliclazide and Lactose at the chromatogram of the manufactured tablets in room temperature and under accelerated stability conditions. This impurity increases at the stability months. By changing Lactose to Mannitol and using Calcium Dibasic Phosphate in the tablet formulation, the amount of the impurity decreased and was in the acceptance range defined by British pharmacopeia for Gliclazide Tablets. This method is a fast and simple way to predict the existence of incompatibility between excipients and active pharmaceutical ingredients.

Keywords: PCA, gliclazide, impurity, infrared spectroscopy, interaction

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Authors: Jamal R. Elbergali

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Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero-Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol (x ̃_(N_T )), then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.

Keywords: SNR, BER, BPSK, MIMO, modulation, zero forcing (ZF), OSIC, ZF-IC, spatial multiplexing (SM)

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Authors: Marcela de Oliveira, Guilherme Giacomini, Allan Felipe Fattori Alves, Ana Luiza Menegatti Pavan, Maria Eugenia Dela Rosa, Fernando Antonio Bacchim Neto, Diana Rodrigues de Pina

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It is estimated that each year one death every 10 seconds (about 2 million deaths) in the world is attributed to tuberculosis (TB). Even after effective treatment, TB leaves sequelae such as, for example, pulmonary fibrosis, compromising the quality of life of patients. Evaluations of the aforementioned sequel are usually performed subjectively by radiology specialists. Subjective evaluation may indicate variations inter and intra observers. The examination of x-rays is the diagnostic imaging method most accomplished in the monitoring of patients diagnosed with TB and of least cost to the institution. The application of computational algorithms is of utmost importance to make a more objective quantification of pulmonary impairment in individuals with tuberculosis. The purpose of this research is the use of computer algorithms to quantify the pulmonary impairment pre and post-treatment of patients with pulmonary TB. The x-ray images of 10 patients with TB diagnosis confirmed by examination of sputum smears were studied. Initially the segmentation of the total lung area was performed (posteroanterior and lateral views) then targeted to the compromised region by pulmonary sequel. Through morphological operators and the application of signal noise tool, it was possible to determine the compromised lung volume. The largest difference found pre- and post-treatment was 85.85% and the smallest was 54.08%.

Keywords: algorithm, radiology, tuberculosis, x-rays exam

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Authors: Yaser E. Greish, Emad F. Hindawy, Maryam S. Al Nehayan

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Introductory courses such as General Chemistry I, General Physics I and General Biology need special attention as students taking these courses are usually at their first year of the university. In addition to the language barrier for most of them, they also face other difficulties if these elementary courses are taught in the traditional way. Changing the routine method of teaching of these courses is therefore mandated. In this regard, podcasting of chemistry lectures was used as an add-on to the traditional and non-traditional methods of teaching chemistry to science and non-science students. Podcasts refer to video files that are distributed in a digital format through the Internet using personal computers or mobile devices. Pedagogical strategy is another way of identifying podcasts. Three distinct teaching approaches are evident in the current literature and include receptive viewing, problem-solving, and created video podcasts. The digital format and dispensing of video podcasts have stabilized over the past eight years, the type of podcasts vary considerably according to their purpose, degree of segmentation, pedagogical strategy, and academic focus. In this regard, the whole syllabus of 'General Chemistry I' course was developed as podcasts and were delivered to students throughout the semester. Students used the podcasted files extensively during their studies, especially as part of their preparations for exams. Feedback of students strongly supported the idea of using podcasting as it reflected its effect on the overall understanding of the subject, and a consequent improvement of their grades.

Keywords: podcasting, introductory course, interactivity, flipped classroom

Procedia PDF Downloads 246

Authors: Daniel E. Martin, Chenghai Yang

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Variable-rate aerial application systems are becoming more readily available; however, aerial applicators typically only use the systems for constant-rate application of materials, allowing the systems to compensate for upwind and downwind ground speed variations. Much of the resistance to variable-rate aerial application system adoption in the U.S. pertains to applicator’s trust in the systems to turn on and off automatically as desired. The objectives of this study were to evaluate a commercially available variable-rate aerial application system under field conditions to demonstrate both the response and accuracy of the system to desired application rate inputs. This study involved planting oats in a 35-acre fallow field during the winter months to establish a uniform green backdrop in early spring. A binary (on/off) prescription application map was generated and a variable-rate aerial application of glyphosate was made to the field. Airborne multispectral imagery taken before and two weeks after the application documented actual field deposition and efficacy of the glyphosate. When compared to the prescription application map, these data provided application system response and accuracy information. The results of this study will be useful for quantifying and documenting the response and accuracy of a commercially available variable-rate aerial application system so that aerial applicators can be more confident in their capabilities and the use of these systems can increase, taking advantage of all that aerial variable-rate technologies have to offer.

Keywords: variable-rate, aerial application, remote sensing, precision application

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Authors: Hao Wang, Shengchun Wang, Weidong Wang

Abstract:

On the influence of train vibration and environmental noise on the measurement of track wear, we proposed a method for automatic extraction of circular arc on the inner or outer side of the rail waist and achieved the high-precision registration of rail profile. Firstly, a polynomial fitting method based on truncated residual histogram was proposed to find the optimal fitting curve of the profile and reduce the influence of noise on profile curve fitting. Then, based on the curvature distribution characteristics of the fitting curve, the interval search algorithm based on dynamic window’s maximum curvature entropy was proposed to realize the automatic segmentation of small circular arc. At last, we fit two circle centers as matching reference points based on small circular arcs on both sides and realized the alignment from the measured profile to the standard designed profile. The static experimental results show that the mean and standard deviation of the method are controlled within 0.01mm with small measurement errors and high repeatability. The dynamic test also verified the repeatability of the method in the train-running environment, and the dynamic measurement deviation of rail wear is within 0.2mm with high repeatability.

Keywords: curvature entropy, profile registration, rail wear, structured light, train-running

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Authors: Ahmad Shahin, Walid Moudani, Ali Bekraki

Abstract:

This research aims at presenting a predictive data mining model to handle an accurate diagnosis of acute appendicitis with patients for the purpose of maximizing the health service quality, minimizing morbidity/mortality, and reducing cost. However, acute appendicitis is the most common disease which requires timely accurate diagnosis and needs surgical intervention. Although the treatment of acute appendicitis is simple and straightforward, its diagnosis is still difficult because no single sign, symptom, laboratory or image examination accurately confirms the diagnosis of acute appendicitis in all cases. This contributes in increasing morbidity and negative appendectomy. In this study, the authors propose to generate an accurate model in prediction of patients with acute appendicitis which is based, firstly, on the segmentation technique associated to ABC algorithm to segment the patients; secondly, on applying fuzzy logic to process the massive volume of heterogeneous and noisy data (age, sex, fever, white blood cell, neutrophilia, CRP, urine, ultrasound, CT, appendectomy, etc.) in order to express knowledge and analyze the relationships among data in a comprehensive manner; and thirdly, on applying dynamic programming technique to reduce the number of data attributes. The proposed model is evaluated based on a set of benchmark techniques and even on a set of benchmark classification problems of osteoporosis, diabetes and heart obtained from the UCI data and other data sources.

Keywords: healthcare management, acute appendicitis, data mining, classification, decision tree

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Authors: Laurisa Peters, Usha Barahmand, Maria Stalias-Mantzikos, Naila Shamsina, Kerry Aguero

Abstract:

In the current study, the authors sought to examine whether the links between moral and sexual disgust and misophonia are mediated by mental contamination. An internationally diverse sample of 283 adults (193 females, 76 males, and 14 non-binary individuals) ranging in age from 18 to 60 years old was recruited from online social media platforms and survey recruitment sites. The sample completed an online battery of scales that consisted of the New York Misophonia Scale, State Mental Contamination Scale, and the Three-Domain Disgust Scale. The hypotheses were evaluated using a series of mediations performed using the PROCESS add-on in SPSS. Correlations were found between emotional and aggressive-avoidant reactions in misophonia, mental contamination, pathogen disgust, and sexual disgust. Moral disgust and non-aggressive reactions in misophonia failed to correlate significantly with any of the other constructs. Sexual disgust had direct and indirect effects, while pathogen disgust had only direct effects on aspects of misophonia. These findings partially support our hypothesis that mental contamination mediates the link between disgust propensity and misophonia while also confirming that pathogen-based disgust is not associated with mental contamination. Findings imply that misophonia is distinct from obsessive-compulsive disorder. Further research into the conceptualization of moral disgust is warranted.

Keywords: misophonia, moral disgust, pathogen disgust, sexual disgust, mental contamination

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