Search results for: spectroscopy data analysis

Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Xiaoping Su, Gabriel G. Malouf

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Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor.

Keywords: lncRNA profiling, breast cancer, HOTAIRM1, tumor suppressor

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Authors: Fatima Gouiferda

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Uncertainty and competitiveness are changing firm’s environment to become more complicated. The competition is moving to supply chain’s level, and firms need to collaborate and innovate to survive. In the current economy, common efforts between organizations and developing new capacities mutually are the key resources in gaining collaborative advantage and enhancing supply chain performance. The purpose of this paper is to explore different practices of collaboration activities that exist in the pharmaceutical industry of Morocco. Also, to inquire how these practices affect supply chain performance. The exploration is based on interpretativism research paradigm. Data were collected through semi-structured interviews from supply chain practitioners. Qualitative data was analyzed via Iramuteq software to explore different themes of the study.The findings include descriptive analysis as a result of data processing using Iramuteq. It also encompasses the content analysis of the themes extracted from interviews.

Keywords: inter-firm relationships, collaboration, supply chain innovation, morocco

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Authors: Yi-Hao Pai, Wen-Feng Chen

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The planting area of Pomelo in Hualien, Taiwan amounts to thousands of hectares. Especially in the blooming season of Pomelo, it is an important producing area for Pomelo honey, and it is also a good test field for promoting the "Under-forest Economy". However, in the current Pomelo garden planting and management operations, the large amount of agricultural waste generated by the pruning of the branches causes environmental sanitation concerns, which can lead to the hiding of pests or the infection of the Pomelo tree, and indirectly increase the health risks of bees. Therefore, how to deal with the pruning of the branches and avoid open burning is a topic of social concern in recent years. In this research, afeasibility study evaluating energy conversion efficiency through agricultural waste gasification from the Pomelo garden, Taiwan, is demonstrated. we used a high-temperature gasifier to convert the pruning of the branches into syngas and biochar. In terms of syngas composition and calorific value assessment, we use the biogas monitoring system for analysis. Then, we used Raman spectroscopy and electron microscopy (EM) to diagnose the microstructure and surface morphology of biochar. The results indicate that the 1 ton of pruning of the branches can produce 1797.03m3 of syngas, corresponding to a calorific value of 9.1MJ/m3. The main components of the gas include CH4, H2, CO, and CO2, and the corresponding gas composition ratio is 16.8%, 7.1%, 13.7%, and 24.5%. Through the biomass syngas generator with a conversion efficiency of 30% for power generation, a total of 1,358kWh can be obtained per ton of pruning of the branches. In the research of biochar, its main characteristics in Raman spectroscopy are G bands and D bands. The first-order G and D bands are at 1580 and 1350 cm⁻¹, respectively. The G bands originates from the in-plane tangential stretching of the C−C bonds in the graphitic structure, and theD band corresponds to scattering from local defects or disorders present in carbon. The area ratio of D and G peaks (D/G) increases with the decrease of reaction temperature. The larger the D/G, the higher the defect concentration and the higher the porosity. This result is consistent with the microstructure displayed by SEM. The study is expected to be able to reuse agricultural waste and promote the development of agricultural and green energy circular economy.

Keywords: agricultural waste, gasification, energy conversion, pomelo garden

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Authors: M. Al Huwayz, A. Salhi, S. Alhassan, S. Alotaibi, A. Almalki, M.Almunyif, A. Alhassni, M. Henini

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Recently, there has been much research carried out to investigate quantum dots (QDs) lasers with the aim to increase the gain of quantum well lasers. However, one of the difficulties with these structures is that electrically active defects can lead to serious issues in the performance of these devices. It is therefore essential to fully understand the types of defects introduced during the growth and/or the fabrication process. In this study, the effects of Gamma radiation on the electrically active defects in p-i-n InAs/InGaAsQDs laser structures grown by Molecular Beam Epitaxy (MBE) technique on GaAs substrates were investigated. Deep Level Transient Spectroscopy (DLTS), current-voltage (I-V), and capacitance-voltage (C-V) measurements were performed to explore these effects on the electrical properties of these QDs lasers. I-V measurements showed that as-grown sample had better electrical properties than the irradiated sample. However, DLTS and Laplace DLTS measurements at different reverse biases revealed that the defects in the-region of the p-i-n structures were decreased in the irradiated sample. In both samples, a trap with an activation energy of ~ 0.21 eV was assigned to the well-known defect M1 in GaAs layers

Keywords: quantum dots laser structures, gamma radiation, DLTS, defects, nAs/IngaAs

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Authors: Abdul Haris Setiawan

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Teachers empowerment is one of the important factors considered to contribute significantly to the achievement of the national education goals. This study was conducted to determine the influence on the vocational teachers empowerment toward the performance of the vocational high schools based on the Education National Standards of Indonesia. The population of the study was all vocational teachers at the State Vocational High schools in Surakarta, Central Java Province, Indonesia. The sampling technique used proportional random sampling technique. This study used a quantitative descriptive statistical analysis techniques. The data was collected using questionnaires. The data has been collected and then tested using analysis requirements test. Having tested using the requirements analysis and then the data processed using regression analysis between the independent and dependent variables to determine the effect and the regression equation. The results of the study found that the level of vocational high schools’ performance based on the Education National Standards of Indonesia was 74.29%, including in the high category; the level of vocational teachers empowerment was 76.20%, including in the high category; there was a positive influence of vocational teachers empowerment toward the vocational high schools’ performance based on the Education National Standards of Indonesia with a correlation coefficient of 0,886, and a contribution of 78.50% with the regression equation Y = 79.431 +0.534 X.

Keywords: vocational teachers, empowerment, vocational high school, the education national standards

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Authors: K. P. Ganesh, T. Gomathi, L. Arul Pragasan

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Degradation of lake water quality is one of the serious environmental threats for the last few decades, particularly, the lakes situated in and around urban and industrial areas. The present study aimed to analyze the physicochemical and biological parameters, and metal elements to determine the water quality of Krishnampathi, Ukkadam, Kurichi, Sulur and Singanallur Lakes. Of the 23 physicochemical parameters analyzed in the five lakes, except TDS, Chloride and Total hardness values all the 20 parameters were found within the prescribed limit as recommended by World Health Organization (WHO) and Bureau of Indian Standards (BIS). In case of biological parameter, both Total Coliform and Fecal Coliform bacteria (Escherichia coli) were identified. This indicates the contamination of lakes by fecal matter, and warns of potential of disease causing by viruses, bacteria and other organisms. Among the twelve metal elements (Al, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Mo, Cd and Pb) determined by inductively coupled plasma-mass spectroscopy, except Cd (for all lakes), and Pb (for Ukkadam, Kurichi, Sulur & Singanallur), all the elements were found above the prescribed limits of BIS. The results of the present study revealed that all the five major lakes of Coimbatore were contaminated. It is recommended that proper implementation of the new wetland waste management system and monitoring of water quality be of the urgent need to sustain the water bodies for future generations.

Keywords: heavy metals, inductively coupled plasma-mass spectroscopy, physicochemical and biological parameters, water quality

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Authors: Poramatdha Chutimant

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The objective of the research was to propose a model of teacher leadership in history instruction for utilization. Everett M. Rogers’ Diffusion of Innovations Theory is applied as theoretical framework. Qualitative method is to be used in the study, and the interview protocol used as an instrument to collect primary data from best practices who awarded by Office of National Education Commission (ONEC). Open-end questions will be used in interview protocol in order to gather the various data. Then, information according to international context of history instruction is the secondary data used to support in the summarizing process (Content Analysis). Dendrogram is a key to interpret and synthesize the primary data. Thus, secondary data comes as the supportive issue in explanation and elaboration. In-depth interview is to be used to collected information from seven experts in educational field. The focal point is to validate a draft model in term of future utilization finally.

Keywords: history study, nationalism, patriotism, responsible citizenship, teacher leadership

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Authors: Debadi Chakraborty, John E. Sader

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Nanomechanical devices have emerged as a versatile platform for a host of applications due to their extreme sensitivity to environmental conditions. For example, mass measurements with sensitivity at the atomic level have recently been demonstrated. Ultrafast laser spectroscopy coherently excite the vibrational modes of metal nanoparticles and permits precise measurement of the vibration characteristics as a function of nanoparticle shape, size and surrounding environment. This study reports that the vibration of metal nanoparticles in simple liquids, like water and glycerol are not described by conventional fluid mechanics, i.e., Navier Stokes equations. The intrinsic molecular relaxation processes in the surrounding liquid are found to have a profound effect on the fluid-structure interaction of mechanical devices at nanometre scales. Theoretical models have been developed based on the non-Newtonian viscoelastic fluid-structure interaction theory to investigate the vibration of nanoparticles immersed in simple fluids. The utility of this theoretical framework is demonstrated by comparison to measurements on single nanowires and ensembles of metal rods. This study provides a rigorous foundation for the use of metal nanoparticles as ultrasensitive mechanical sensors in fluid and opens a new paradigm for understanding extremely high frequency fluid mechanics, nanoscale sensing technologies, and biophysical processes.

Keywords: fluid-structure interaction, nanoparticle vibration, ultrafast laser spectroscopy, viscoelastic damping

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Lele Zhang, Hui Leng Choo, Alexander Konyukhov, Shuguang Li

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China is currently the world's largest producer and distributor of electric bicycle (e-bike). The increasing number of e-bikes on the road is accompanied by rising injuries and even deaths of e-bike drivers. Therefore, there is a growing need to improve the safety structure of e-bikes. This 3D frictionless contact analysis is a preliminary, but necessary work for further structural design improvement of an e-bike. The contact analysis between e-bike and the ground was carried out as follows: firstly, the Penalty method was illustrated and derived from the simplest spring-mass system. This is one of the most common methods to satisfy the frictionless contact case; secondly, ANSYS static analysis was carried out to verify finite element (FE) models with contact pair (without friction) between e-bike and the ground; finally, ANSYS transient analysis was used to obtain the data of the penetration p(u) of e-bike with respect to the ground. Results obtained from the simulation are as estimated by comparing with that from theoretical method. In the future, protective shell will be designed following the stability criteria and added to the frame of e-bike. Simulation of side falling of the improved safety structure of e-bike will be confirmed with experimental data.

Keywords: frictionless contact, penalty method, e-bike, finite element

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: G. Çelik Gül, F. Kurtuluş

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Colemanite is the most common borate mineral, and the main source of the boron required by plants, human, and earth. Transition metals exhibit optical and physical properties such as; non-linear optical character, structural diversity, thermal stability, long cycle life and luminescent radiation. The doping of colemanite with a transition metal, bring it very interesting and attractive properties which make them applicable in industry. Iron doped calcium borate was synthesized by conventional solid state method at 1200 °C for 12 h with a systematic pathway. X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy/energy dispersive analyze (SEM/EDS) were used to characterize structural and morphological properties. Also, thermal properties were recorded by thermogravimetric-differential thermal analysis (TG/DTA). 

Keywords: colemanite, conventional synthesis, powder x-ray diffraction, borates

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Authors: Nidhi Adhlakha, K. L. Yadav

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Three phase magnetoelectric (ME) composites (1-x)(0.7BiFeO3-0.3CoFe2O4)-xPbTiO3 (or equivalently written as (1-x)(0.7BFO-0.3CFO)-xPT) with x variations 0, 0.30, 0.35, 0.40, 0.45 and 1.0 were synthesized using hybrid processing route. The effects of PT addition on structural, multiferroic and optical properties have been subsequently investigated. A detailed Rietveld refinement analysis of X-ray diffraction patterns has been performed, which confirms the presence of structural phases of individual constituents in the composites. Field emission scanning electron microscopy (FESEM) images are taken for microstructural analysis and grain size determination. Transmission electron microscopy (TEM) analysis of 0.3CFO-0.7BFO reveals the average particle size to be lying in the window of 8-10 nm. The temperature dependent dielectric constant at various frequencies (1 kHz, 10 kHz, 50 kHz, 100 kHz and 500 kHz) has been studied and the dielectric study reveals that the increase of dielectric constant and decrease of average dielectric loss of composites with incorporation of PT content. The room temperature ferromagnetic behavior of composites is confirmed through the observation of Magnetization vs. Magnetic field (M-H) hysteresis loops. The variation of magnetization with temperature indicates the presence of spin glass behavior in composites. Magnetoelectric coupling is evidenced in the composites through the observation of the dependence of the dielectric constant on the magnetic field, and magnetodielectric response of 2.05 % is observed for 45 mol% addition of PT content. The fractional change of magnetic field induced dielectric constant can also be expressed as ∆ε_r~γM^2 and the value of γ is found to be ~1.08×10-2 (emu/g)-2 for composite with x=0.40. Fourier transformed infrared (FTIR) spectroscopy of samples is carried out to analyze various bonds formation in the composites.

Keywords: composite, X-ray diffraction, dielectric properties, optical properties

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Authors: Stephanie Boue, Nicolas Sierro, Julia Hoeng, Manuel C. Peitsch

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Systems biology relies on large numbers of data points and sophisticated methods to extract biologically meaningful signal and mechanistic understanding. For example, analyses of transcriptomics and proteomics data enable to gain insights into the molecular differences in tissues exposed to diverse stimuli or test items. Whereas the interpretation of endpoints specifically measuring a mechanism is relatively straightforward, the interpretation of big data is more complex and would benefit from comparing results obtained with diverse analysis methods. The sbv IMPROVER project was created to implement solutions to verify systems biology data, methods, and conclusions. Computational challenges leveraging the wisdom of the crowd allow benchmarking methods for specific tasks, such as signature extraction and/or samples classification. Four challenges have already been successfully conducted and confirmed that the aggregation of predictions often leads to better results than individual predictions and that methods perform best in specific contexts. Whenever the scientific question of interest does not have a gold standard, but may greatly benefit from the scientific community to come together and discuss their approaches and results, datathons are set up. The inaugural sbv IMPROVER datathon was held in Singapore on 23-24 September 2016. It allowed bioinformaticians and data scientists to consolidate their ideas and work on the most promising methods as teams, after having initially reflected on the problem on their own. The outcome is a set of visualization and analysis methods that will be shared with the scientific community via the Garuda platform, an open connectivity platform that provides a framework to navigate through different applications, databases and services in biology and medicine. We will present the results we obtained when analyzing data with our network-based method, and introduce a datathon that will take place in Japan to encourage the analysis of the same datasets with other methods to allow for the consolidation of conclusions.

Keywords: big data interpretation, datathon, systems toxicology, verification

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Authors: Butta Singh

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This paper presents a chaotic map based approach for secured embedding of patient’s confidential data in electrocardiogram (ECG) signal. The chaotic map generates predefined locations through the use of selective control parameters. The sample value difference method effectually hides the confidential data in ECG sample pairs at these predefined locations. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through various statistical and clinical performance measures. Statistical metrics comprise of Percentage Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR). Further, a comparative analysis between proposed method and existing approaches was also performed. The results clearly demonstrated the superiority of proposed method.

Keywords: chaotic maps, ECG steganography, data embedding, electrocardiogram

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Authors: Mehmet Durnali

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The main purpose of this study is to find out, analyze and discuss basic principles of education and training in the constitutions, including the latest amendment, of France, Indonesia, Japan, South Africa, the United States of America, and Turkey. This research specifically aims at establishing a framework in order to compare educational values such as right of education, responsibilities of states and those of people, and other issues pertaining to education in the Constitution of Turkey to others. Additionally, it emphasizes the meaning of education in constitution, the reasons for references to education in constitutions and why it is important for people, states or nations and state organs. Qualitative analysis technique is performed to accomplish the aim of this study. Maximum variation sampling is used. The main data source of the analysis is official organic laws of those countries. The data is examined by using descriptive and content analysis method.

Keywords: education in the constitution, education law, legal principles of education, right to education

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Authors: Anahita Mortazavi Manesh, Mojtaba Bagherzadeh

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Metalloporphyrins are well known to mimic the activity of monooxygenase enzymes. In this regard, metalloporphyrin complexes have been largely employed as valuable biomimetic catalysts, owing to the critical roles they play in oxygen transfer processes in catalytic oxidation reactions. Investigating in this area is based on different strategies to design selective, stable and high turnover catalytic systems. Immobilization of expensive metalloporphyrin catalysts onto supports appears to be a good way to improve their stability, selectivity and the catalytic performance because of the support environment and other advantages with respect to recovery, reuse. In other words, supporting metalloporphyrins provides a physical separation of active sites, thus minimizing catalyst self-destruction and dimerization of unhindered metalloporphyrins. Furthermore, heterogeneous catalytic oxidations have become an important target since their process are used in industry, helping to minimize the problems of industrial waste treatment. Hence, the immobilization of these biomimetic catalysts is much desired. An attractive approach is the preparation of the heterogeneous catalyst involves immobilization of complexes on silica coated magnetic nano-particles. Fe3O4@SiO2 magnetic nanoparticles have been studied extensively due to their superparamagnetism property, large surface area to volume ratio and easy functionalization. Using heterogenized homogeneous catalysts is an attractive option to facile separation of catalyst, simplified product work-up and continuity of catalytic system. Homogeneous catalysts immobilized on magnetic nanoparticles (MNPs) surface occupy a unique position due to combining the advantages of both homogeneous and heterogeneous catalysts. In addition, superparamagnetic nature of MNPs enable very simple separation of the immobilized catalysts from the reaction mixture using an external magnet. In the present work, an efficient heterogeneous catalyst was prepared by immobilizing manganese porphyrin on functionalized magnetic nanoparticles through the amino propyl linkage. The prepared catalyst was characterized by elemental analysis, FT-IR spectroscopy, X-ray powder diffraction, atomic absorption spectroscopy, UV-Vis spectroscopy, and scanning electron microscopy. Application of immobilized metalloporphyrin in the oxidation of various organic substrates was explored using Gas chromatographic (GC) analyses. The results showed that the supported Mn-porphyrin catalyst (Fe3O4@SiO2-NH2@MnPor) is an efficient and reusable catalyst in oxidation reactions. Our catalytic system exhibits high catalytic activity in terms of turnover number (TON) and reaction conditions. Leaching and recycling experiments revealed that nanocatalyst can be recovered several times without loss of activity and magnetic properties. The most important advantage of this heterogenized catalytic system is the simplicity of the catalyst separation in which the catalyst can be separated from the reaction mixture by applying a magnet. Furthermore, the separation and reuse of the magnetic Fe3O4 nanoparticles were very effective and economical.

Keywords: Fe3O4 nanoparticle, immobilized metalloporphyrin, magnetically separable nanocatalyst, oxidation reactions

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Authors: R. S. Yadav, J. Havlica, I. Kuřitka, Z. Kozakova, J. Masilko, M. Hajdúchová, V. Enev, J. Wasserbauer

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In this present work, CoFe2-xNdxO4 (0.0 ≤ x ≥0.1) spinel ferrite nanoparticles were synthesized by starch-assisted sol-gel auto-combustion method. Powder X-ray diffraction patterns were revealed the formation of cubic spinel ferrite with the signature of NdFeO3 phase at higher Nd3+ concentration. The field emission scanning electron microscopy study demonstrated the spherical nanoparticle in the size range between 5-15 nm. Raman and Fourier Transform Infrared spectra supported the formation of the spinel ferrite structure in the nanocrystalline form. The X-ray photoelectron spectroscopy (XPS) analysis confirmed the presence of Co2+ and Fe3+ at octahedral as well as a tetrahedral site in CoFe2-xNdxO4 nanoparticles. The change in magnetic properties with a variation of concentration of Nd3+ ions in cobalt ferrite nanoparticles was observed.

Keywords: nanoparticles, spinel ferrites, sol-gel auto-combustion method, CoFe2-xNdxO4

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Lingerew Nebere Kassie, Ping-Yu Chang, Hsin-Hua Huang, , Chaw-Son Chen

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Transient electromagnetic (TEM) data plays a crucial role in hydrogeological and environmental applications, providing valuable insights into geological structures and resistivity variations. However, the presence of noise often hinders the interpretation and reliability of these data. Our study addresses this issue by utilizing a FASTSNAP system for the TEM survey, which operates at different modes (low, medium, and high) with continuous adjustments to discretization, gain, and current. We employ a denoising approach that processes the raw data obtained from each acquisition mode to improve signal quality and enhance data reliability. We use a signal-averaging technique for each mode, increasing the signal-to-noise ratio. Additionally, we utilize wavelet transform to suppress noise further while preserving the integrity of the underlying signals. This approach significantly improves the data quality, notably suppressing severe noise at late times. The resulting denoised data exhibits a substantially improved signal-to-noise ratio, leading to increased accuracy in parameter estimation. By effectively denoising TEM data, our study contributes to a more reliable interpretation and analysis of underground structures. Moreover, the proposed denoising approach can be seamlessly integrated into existing ground-based TEM data processing workflows, facilitating the extraction of meaningful information from noisy measurements and enhancing the overall quality and reliability of the acquired data.

Keywords: data quality, signal averaging, transient electromagnetic, wavelet transform

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Authors: Naveeda Akhtar Qureshi, Wajiha

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Coccidiosis is a protozoan parasitic disease of genus Eimeria. It is an avian infection causing a great economic loss of 3 billion USD per year globally. A number of anticoccidial drugs are in use however many of them have side effects and cost effective. With increase in poultry demand throughout the world there is a need of more drugs and vaccines against coccidiosis. The present study is based upon the use of F. racemosa a medicinal plant to be a potential source of anticoccidial agents. The methanolic leaves extract was fractionated by column and thin layer chromatography and got nineteen fractions. Each fraction different concentrations was evaluated for its anticoccidial properties in an invitro experiment against E. tenella, E. necatrix and E. mitis. The anticoccidial active fractions were further characterized by spectroscopy (UV-Vis, FTIR) and GC-MS analysis. The in silico molecular docking of active fractions identified compounds were carried out. Among all fractions significantly maximum sporulation inhibition efficacy was shown by F-19 (67.11±2.18) followed by F-15 (65.21±1.34) at concentration of 30mg/ml against E. tenella. The significantly highest sporozoites viability inhibition was shown by F-19 (69.23±2.11) followed by F-15 (67.14±1.52) against E. necatrix at concentration 30mg/ml. Anticoccidial active fractions 15 and 19 showed peak spectrum at 207 and 202nm respectively by UV analysis. Their FTIR analysis confirmed the presence of carboxylic acid, amines, phenols, etc. Anticoccidial active compounds like Cyclododecane methanol, oleic acid, Octadecanoic acid, etc were identified by GC-MS analysis. Identified compounds in silico molecular docking study showed that cyclododecane methanol of F-19 and oleic acid of F-15 showed highest binding affinity with target S-Adenosylmethionine synthase. Hence for further authentication in vivo anticoccidial studies are recommended.

Keywords: ficus racemosa, cluster fig, column chromatography, anticoccidial fractions, GC-MS, molecular docking., s-adenosylmethionine synthase

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Authors: Elmineh Tsegahun Gedif

Abstract:

Zinc oxide nanoparticles (ZnO NPs) have attracted huge attention due to catalytic, optical, photonic, and antibacterial activity. Zinc oxide nanoparticles were successfully synthesized via a fast, non-toxic, cost-effective, and eco-friendly method by biologically reducing Zn(NO3)2.6H2O solution with Neem (Azadirachta indica) leaf extract under optimum conditions (pH = 9). The presence of active flavonoids, phenolic groups, alkaloids, terpenoids, and tannins, which were in the biomass of the Neem leaf extract before and after reduction, was identified using qualitative screening methods (observing the color changes) and FT-IR Spectroscopy. The formation of ZnO NPs was visually indicated by the color changes from colorless to light yellow color. Biosynthesized nanoparticles were also characterized by UV-visible, FT-IR, and XRD spectroscopies. The reduction process was simple and convenient to handle and was monitored by UV-visible spectroscopy that showed surface plasmon resonance (SPR) of the ZnO NPs at 321 nm. This result clearly revealed the formation of ZnO NPs. X-ray diffraction was used to investigate the crystal structure. The average particle size of ZnO powder and around 20 nm using the line width of the plane, and the refraction peak using Scherrer’s equation. The synthesized zinc oxide nanoparticles were evaluated for antimicrobial activities against Gram-positive and Gram-negative bacteria. Zinc nanoparticles exhibited the maximum zone of inhibition against Escherichia coli (15 mm), while the least activity was seen against Staphylococcus aureus.

Keywords: antimicrobial activity, azadirachta indica, green synthesis, ZnO NPs

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Authors: Rimzhim Gupta, Bhanupriya Boruah, Jayant M. Modak, Giridhar Madras

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Microbial contamination is one of the major concerns in the field of water treatment. AOP (advanced oxidation processes) is well-established method to resolve the issue of removal of contaminants in water. A Z-scheme composite g-C₃N₄/CuWO₄ was synthesized by sol-gel method for the photocatalytic inactivation of a mixed population of Gram-positive bacteria (S. aureus) and Gram-negative bacteria (E. coli). The photoinactivation was observed for different types of bacteria in the same medium together and individually in the absence of the nutrients. The lattice structures and phase purities were determined by X-ray diffraction. For morphological and topographical features, scanning electron microscopy and transmission electron microscopy analyses were carried out. The band edges of the semiconductor (valence band and conduction band) were determined by ultraviolet photoelectron microscopy. The lifetime of the charge carriers and band gap of the semiconductors were determined by time resolved florescence spectroscopy and diffused reflectance spectroscopy, respectively. The effect of weight ratio of C₃N₄ and CuWO₄ was observed by performing photocatalytic experiments. To investigate the exact mechanism and major responsible radicals for photocatalysis, scavenger studies were performed. The rate constants and order of the inactivation reactions were obtained by power law kinetics. For E. coli and S. aureus, the order of reaction and rate constants are 1.15, 0.9 and 1.39 ± 0.03 (CFU/mL)⁻⁰.¹⁵ h⁻¹, 47.95 ± 1.2 (CFU/mL)⁰.¹ h⁻¹, respectively.

Keywords: z-scheme, E. coli, S. aureus, sol-gel

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Authors: Aofan Liu, Qianqian Tan, Burra Venkata Durga Kumar

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Data security is of the utmost importance in the healthcare area, as sensitive patient information is constantly sent around and analyzed by many different parties. The use of federated learning, which enables data to be evaluated locally on devices rather than being transferred to a central server, has emerged as a potential solution for protecting the privacy of user information. To protect against data breaches and unauthorized access, federated learning alone might not be adequate. In this context, the application of blockchain technology could provide the system extra protection. This study proposes a distributed federated learning system that is built on blockchain technology in order to enhance security in healthcare. This makes it possible for a wide variety of healthcare providers to work together on data analysis without raising concerns about the confidentiality of the data. The technical aspects of the system, including as the design and implementation of distributed learning algorithms, consensus mechanisms, and smart contracts, are also investigated as part of this process. The technique that was offered is a workable alternative that addresses concerns about the safety of healthcare while also fostering collaborative research and the interchange of data.

Keywords: data privacy, distributed system, federated learning, machine learning

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Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

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Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

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Authors: Francky Fouedjio

Abstract:

Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

Abstract:

Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

Procedia PDF Downloads 859

Authors: Atia Alam

Abstract:

Growing liberalisation and intense market competition increase firm’s risk exposure and induce corporations to use derivatives extensively as a risk management instrument, which results in decrease in firm’s risk, and increase in value. Present study contributes towards existing literature by providing an in-depth analysis regarding the effect of extent of derivative usage on firm’s risk and value by using panel data models and seemingly unrelated regression technique. New evidence is established in current literature by dividing the sample data based on firm’s Exchange Rate (ER) and Interest Rate (IR) exposure. Analysis is performed for the effect of extent of derivative usage on firm’s risk and value and its variation with respect to the ER and IR exposure. Sample data consists of 166 Pakistani firms listed on Pakistan stock exchange for the period of 2004-2010. Results show that extensive usage of derivative instruments significantly increases firm value and reduces firm’s risk. Furthermore, comprehensive analysis depicts that Pakistani corporations having higher exchange rate exposure, with respect to foreign sales, and higher interest rate exposure, on the basis of industry adjusted leverage, have higher firm value and lower risk. Findings from seemingly unrelated regression also provide robustness to results obtained through panel data analysis. Study also highlights the role of derivative usage as a risk management instrument in high and low ER and IR risk and helps practitioners in understanding how value increasing effect of extent of derivative usage varies with the intensity of firm’s risk exposure.

Keywords: extent of derivative usage, firm value, risk, Pakistan, non-financial firms

Procedia PDF Downloads 339