Search results for: physiological data extraction

Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Célia Barreto Carvalho, Carolina da Motta, Marina Sousa, Joana Cabral, Ana Luísa Carvalho, Ermelindo Peixoto

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The aim of the current study was to develop and validate a Response to Stressful Situations Scale (RSSS) for the Portuguese population. This scale assesses the degree of stress experienced in scenarios that can constitute positive, negative and more neutral stressors, and also describes the physiological, emotional and behavioral reactions to those events according to their intensity. These scenario include typical stressor scenarios relevant to patients with schizophrenia, which are currently absent from most scale, assessing specific risks that these stressors may bring on subjects, which may prove useful in non-clinical and clinical populations (i.e. patients with mood or anxiety disorders, schizophrenia). Results from Principal Components Analysis and Confirmatory Factor Analysis of on two adult samples from general population allowed to confirm a three-factor model with good fit indices: χ2 (144)= 370.211, p = 0.000; GFI = 0.928; CFI = 0.927; TLI = 0.914, RMSEA = 0.055, P( rmsea ≤ 0.005) = 0.096; PCFI = 0.781. Further data analysis on the scale revealed that RSSS is an adequate assessment tool of stress response in adults to be used in further research and clinical settings, with good psychometric characteristics, adequate divergent and convergent validity, good temporal stability and high internal consistency.

Keywords: assessment, stress events, stress response, stress vulnerability

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Authors: Rungnapa Chantra

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As health care rapidly changes, the nursing profession is also evolving to improve quality of care while maintaining competency in their practice. The purpose of this study was to investigate the factors of disaster nurse competencies and investigate the predictable variables in disaster nurse competencies in Suratthani Province, Thailand. The sample consisted of 305 nurses who were recruited by simple random sampling. The development questionnaires from ICN Framework and research contains Pre/Mitigation, Preparedness, Response and Recovery/Rehabilitation Competencies (α=0.87). The data were analyzed using Principle Components Extraction and Orthogonal Rotation with Varimax Method. The findings were as follows; four significant factors of disaster nurse competencies in Suratthani Province, Thailand were identified. These factors were described by 62 variables that accounted for 50.01% of the total variance. The results of this study could be for agencies that are responsible for the development of nursing competencies and should be aware of the development of knowledge and skills in disaster management.

Keywords: disaster nursing competency of nurses, nursing informatics, health science, medical

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Authors: Sharareh Mohseni

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Purpose: The objective of this study was to find a suitable solvent to produce saffron edible extract with improved chemical properties. Design/methodology/approach: Dried and pulverized stigmas of C. sativus L. (10g) was extracted with 300 ml of solvents including: distillated water (DW), ethanol/DW, methanol/DW, propylene glycol/DW, heptan/DW, and hexan/DW, for 3 days at 25°C and then centrifuged at 3000 rpm. Then the extracts were evaporated using rotary evaporator at 40°C. The fiber and solvent-free extracts were then analyzed by UV spectrophotometer to detect saffron quality parameters including crocin, picrocrocin and safranal. Findings: Distilled water/ethanol mixture as the extraction solvent, caused larger amounts of the plant constituents to diffuse out to the extract compared to other treatments and also control. Polar solvents including distilled water, ethanol, and propylene glycol (except methanol) were more effective in extracting crocin, picrocrocin, and saffranal than non-polar solvents. Social implications: Due to an enhancement of color and flavor, saffron extract is economical compared to natural saffron. Saffron Extract saves on preparation time and reduces the amount of saffron required for imparting the same flavor, as compared to dry saffron. Liquid extract is easier to use and standardize in food preparations compared to dry stamens and can be dosed precisely compared to natural saffron. Originality/value: No research had been done on production of saffron edible extract using the solvent studied in this survey. The novelty of this research is high and the results can be used industrially.

Keywords: Crocus sativus L., saffron extract, solvent extraction, distilled water

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Serpa A. M., Gómez Hoyos C., Velásquez-Cock J. A., Ruiz L. F., Vélez Acosta L. M., Gañan P., Zuluaga R.

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Turmeric (Curcuma Longa L) is an Indian rhizome known for its biological properties, derived from its active compounds such as curcuminoids. Curcumin, the main polyphenol in turmeric, only represents around 3.5% of the dehydrated rhizome and extraction yields between 41 and 90% have been reported. Therefore, for every 1000 tons of turmeric powder used for the extraction of curcumin, around 970 tons of residues are generated. The present study evaluates the effect of different mechanical treatments (waring blender, grinder and high-pressure homogenization) on the physical and chemical properties of turmeric, as an alternative for the transformation of the entire rhizome. Suspensions of turmeric (10, 20 y 30%) were processed by waring blender during 3 min at 12000 rpm, while the samples treated by grinder were processed evaluating two different Gaps (-1 and -1,5). Finally, the process by high-pressure homogenization, was carried out at 500 bar. According to the results, the luminosity of the samples increases with the severity of the mechanical treatment, due to the stabilization of the color associated with the inactivation of the oxidative enzymes. Additionally, according to the microstructure of the samples, the process by grinder (Gap -1,5) and by high-pressure homogenization allowed the largest size reduction, reaching sizes up to 3 m (measured by optical microscopy). This processes disrupts the cells and breaks their fragments into small suspended particles. The infrared spectra obtained from the samples using an attenuated total reflectance accessory indicates changes in the 800-1200 cm⁻¹ region, related mainly to changes in the starch structure. Finally, the thermogravimetric analysis shows the presence of starch, curcumin and some minerals in the suspensions.

Keywords: characterization, mechanical treatments, suspensions, turmeric rhizome

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: Saima Ali, Liaqat Ali, Nasir Orakzai

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Background: Hydronephrosis is the most common abnormal finding in the urinary tract on prenatal screening with Ultrasonography. The prenatal hydronephrosis is a diagnostic dilemma in differentiating between obstructive variant versus physiologic hydronephrosis. The assessment and prompt diagnosis of prenatal hydronephrosis is important because of the fact that untreated obstructive hydronephrosis usually leads to recurrent UTI, Urosepsis, deterioration of renal functions, non-functioning kidneys, and even end-stage renal disease. Objectives: To determine the etiology and outcome of postnatal treatment of children with prenatal hydronephrosis in two teaching hospitals of Khyber Pakhtunkhwa (KPK) Methods: It is a multicentric descriptive study that was conducted in department of Paediatrics in Kuwait teaching hospital Peshawar and Department of Urology in Institute of Kidney Diseases Hayatabad Medical Complex Peshawar from January 2008 till December 2010. Total numbers of 64 neonates were included in the study with the mean follow-up of 14.5 months. All the diagnostic data in prenatal, postnatal data, and operative and non-operative data were collected on structured Proforma and was analyzed on SPSS version 17. Results: Out of 64 patients, 39 (60.9 %) were male while 25 were female. 52 patients had unilateral while 12 patients had bilateral hydronephrosis. Based upon prenatal USG in term of AP diameter, 37 (57 %) patients had mild hydronephrosis (5-10 mm AP diameter), 14 patients had moderate hydronephrosis (10-15 mm AP diameter) while 13 patients had gross hydronephrosis (More than 15mm). Regarding etiology, 44(76 %) patients were labeled as physiologic hydronephrosis, 11 patients (9.3%) with PUJ obstruction, 5 patients with Vesicoureteric reflux (VUR) and 4 patients with posterior urethral valves. Surgery was performed in total of 15 (23.4%) patients that included open Pyeloplasty in 11 patients, Vesicostomy followed by posterior valve fulguration in 4 patients. All the patients of VUR treated medically. The severity in the grade of prenatal hydronephrosis is significantly associated with the need for definitive urological surgery p < 0.005. Ancillary procedures like percutaneous nephrostomy (PCN) were inserted 7 patients. Conclusions: Prenatal hydronephrosis is a common ailment associated with significant morbidity. Physiological Hydronephrosis and VUR can be successfully treated with medical treatment. However obstructive PUJ obstructions and posterior urethral valves require surgical correction with a good success rate.

Keywords: prenatal hydronephrosis, Pelviureteric Junction (PUJ) Obstruction, vesicoureteric reflux, posterior urethral valve, renography

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Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Imad H. Hameed, Mohammad A. Jebor, Ammera J. Omer

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The aim of this research is to study the mitochonderial coding region by using the Sanger sequencing technique and establish the degree of variation characteristic of a fragment. FTA® Technology (FTA™ paper DNA extraction) utilized to extract DNA. Portion of coding region encompassing positions 11719 –12384 amplified in accordance with the Anderson reference sequence. PCR products purified by EZ-10 spin column then sequenced and Detected by using the ABI 3730xL DNA Analyzer. Five new polymorphic positions 11741, 11756, 11878, 11887 and 12133 are described may be suitable sources for identification purpose in future. The calculated value D= 0.95 and RMP=0.048 of the genetic diversity should be understood as high in the context of coding function of the analysed DNA fragment. The relatively high gene diversity and a relatively low random match probability were observed in Iraq population. The obtained data can be used to identify the variable nucleotide positions characterized by frequent occurrence which is most promising for various identifications.

Keywords: coding region, Iraq, mitochondrial DNA, polymorphic positions, sanger technique

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Authors: Matheus Madureira Matos, Alexandre Rodrigues Farias

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Magnetic Resonance Imaging (MRI) is a vital imaging technique used in both clinical and pre-clinical areas to obtain detailed anatomical and functional information. However, MRI scans can be expensive, time-consuming, and often require the use of anesthetics to keep animals still during the imaging process. Anesthetics are commonly administered to animals undergoing MRI scans to ensure they remain still during the imaging process. However, prolonged or repeated exposure to anesthetics can have adverse effects on animals, including physiological alterations and potential toxicity. Minimizing the duration and frequency of anesthesia is, therefore, crucial for the well-being of research animals. In recent years, various sampling trajectories have been investigated to reduce the number of MRI measurements leading to shorter scanning time and minimizing the duration of animal exposure to the effects of anesthetics. Compressed sensing (CS) and sampling trajectories, such as cartesian, spiral, and radial, have emerged as powerful tools to reduce MRI data while preserving diagnostic quality. This work aims to apply CS and cartesian, spiral, and radial sampling trajectories for the reconstruction of MRI of the abdomen of mice sub-sampled at levels below that defined by the Nyquist theorem. The methodology of this work consists of using a fully sampled reference MRI of a female model C57B1/6 mouse acquired experimentally in a 4.7 Tesla MRI scanner for small animals using Spin Echo pulse sequences. The image is down-sampled by cartesian, radial, and spiral sampling paths and then reconstructed by CS. The quality of the reconstructed images is objectively assessed by three quality assessment techniques RMSE (Root mean square error), PSNR (Peak to Signal Noise Ratio), and SSIM (Structural similarity index measure). The utilization of optimized sampling trajectories and CS technique has demonstrated the potential for a significant reduction of up to 70% of image data acquisition. This result translates into shorter scan times, minimizing the duration and frequency of anesthesia administration and reducing the potential risks associated with it.

Keywords: compressed sensing, magnetic resonance, sampling trajectories, small animals

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Authors: Soomal Hamza, Uzma Imran

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Organochlorine pesticides bio-accumulate into the fat of fish, birds, and animals through which it enters the human food cycle. Due to their persistence and stability in the environment, many health impacts are associated with them, most of which are carcinogenic in nature. In this study, the level of organochlorine pesticides has been detected in Keenjhar Lake and remediated using Rhizoremediation technique. 14 OC pesticides namely, Aldrin, Deldrin, Heptachlor, Heptachlor epoxide, Endrin, Endosulfun I and II, DDT, DDE, DDD, Alpha, Beta, Gamma BHC and two plants namely, Water Hyacinth and Slvinia Molesta were used in the system using pot experiment which processed for 11 days. A consortium was inoculated in both plants to increase its efficiency. Water samples were processed using liquide-liquid extraction. Sediments and roots samples were processed using Soxhlet method followed by clean-up and Gas Chromatography. Delta-BHC was the predominantly found in all samples with mean concentration (ppb) and standard deviation of 0.02 ± 0.14, 0.52 ± 0.68, 0.61 ± 0.06, in Water, Sediments and Roots samples respectively. The highest levels were of Endosulfan II in the samples of water, sediments and roots. Water Hyacinth proved to be better bioaccumulaor as compared to Silvinia Molesta. The pattern of compounds reduction rate by the end of experiment was Delta-BHC>DDD > Alpha-BHC > DDT> Heptachlor> H.Epoxide> Deldrin> Aldrin> Endrin> DDE> Endosulfun I > Endosulfun II. Not much significant difference was observed between the pots with the consortium and pots without the consortium addition. Phytoremediation is a promising technique, but more studies are required to assess the bioremediation potential of different aquatic plants and plant-endophyte relationship.

Keywords: aquatic plant, bio remediation, gas chromatography, liquid liquid extraction

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Authors: Moin Uddin, M. Masroor A. Khan, Faisal Rasheed, Tariq Ahmad Dar, Akbar Ali, Lalit Varshney

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Oligomers, obtained by exposing the natural polysaccharides (alginate, carrageenan, chitosan, etc.) to cobalt-60 generated gamma radiation may prove as potent plant growth promoters when applied as foliar sprays to the plants. They function as endogenous growth elicitors, triggering the synthesis of different enzymes and modulating various plant responses by exploiting the gene expression. Exogenous application of Jasmonic acid or of its methyl ester, methyl jasmonate (MeJ) has been reported to increase the secondary metabolites production in medicinal and aromatic plants. Keeping this in mind, three pot experiments were conducted to test whether the foliar application of irradiated-chitosan (IC) and MeJ, applied alone or in combination, could augment the active constituents as well as growth, physiological and yield attributes of Catharanthus roseus, which carries anticancer alkaloids, viz. vincristine and vinblastine, in its leaves in addition to various other useful alkaloids. Totally, 5 spray treatments, comprising various aqueous solutions of IC [20, 40, 80 and 160 mg L-1 (Experiment 1)], MeJ (10, 20, 30 and 40 mg L-1 (Experiment 2)] and those of IC+MeJ [40+20, 40+30, 80+20, 80+30, 160+20 and 160+30 mg L-1 (Experiment 3)], were applied at seven days interval. Total leaf-alkaloids content as well as growth, physiological and yield parameters, evaluated at 120 days after sowing, were significantly enhanced by IC application. IC application could not increase the leaf-content of vincristine and vinblastine; nonetheless, it significantly augmented the yield of these alkaloids owing to enhancing the dry mass of leaves per plant. MeJ application, particularly at 30 mg L-1, increased both content (17%) and yield (48%) of total leaf-alkaloids as well as the content and yield of vincristine ( 29 and 63%, respectively) and vinblastine (14 and 44%, respectively) alkaloids, though it significantly decreased most other parameters studied, particularly at higher concentrations (30 and 40 mg L-1 of MeJ). As compared to the control (water-spray treatment), collective application of IC (80 mg L-1) and MeJ (20 mg L-1) resulted in the highest values of most of the parameters studied. However, 80 mg L-1 of IC applied with 30 mg L-1 of MeJ gave the best results for the content and yield of total as well as anticancer leaf-alkaloids (vincristine and vinblastine). Comparing the control, it increased the content and yield of total leaf-alkaloids (37 and 118%, respectively) and those of vincristine (65 and 163%, respectively) and vinblastine (31 and 107%, respectively). Conclusively, the applied technique significantly enhanced the production of total as well as anticancer alkaloids of Catharanthus roseus.

Keywords: anticancer alkaloids (vincristine and vinblastine), catharanthus roseus, irradiated chitosan, methyl jasmonate

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Authors: Jaruwan Duangchuen, Siwalak Pathaveerat

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Coconut (Cocos nucifera) belongs to the family Arecaceae. Coconut juice and meat are consumed as food and dessert in several regions of the world. Coconut juice contains low proteins, and arginine is the main amino acid content. Coconut meat is the endosperm of coconut that has nutritional value. It composes of carbohydrate, protein and fat. The objective of this study is utilization of by-products from the virgin coconut oil extraction process by using the skimmed coconut milk as a powder. The skimmed coconut milk was separated from the coconut milk in virgin coconut oil extraction process that consists approximately of protein 6.4%, carbohydrate 7.2%, dietary fiber 0.27 %, sugar 6.27%, fat 3.6 % and moisture content of 86.93%. This skimmed coconut milk can be made to powder for value - added product by using spray drying. The factors effect to the yield and properties of dry skimmed coconut milk in spraying process are inlet, outlet air temperature and the maltodextrin concentration. The percentage of maltodextrin content (15, 20%), outlet air temperature (80 ºC, 85 ºC, 90 ºC) and inlet air temperature (190 ºC, 200 ºC, 210 ºC) were conducted to the skimmed coconut milk spray drying process. The spray dryer was kept air flow rate (0.2698 m3 /s). The result that shown 2.22 -3.23% of moisture content, solubility, bulk density (0.4-0.67g/mL), solubility, wettability (4.04 -19.25 min) for solubility in the water, color, particle size were analyzed for the powder samples. The maximum yield (18.00%) of spray dried coconut milk powder was obtained at 210 °C of temperature, 80°C of outlet temperature and 20% maltodextrin for 27.27 second for drying time. For the amino analysis shown that the high amino acids are Glutamine (16.28%), Arginine (10.32%) and Glycerin (9.59%) by using HPLP method (UV detector).

Keywords: skimmed coconut milk, spray drying, virgin coconut oil process (VCO), maltodextrin

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Yuyan Yi, Jingyi Zheng, Nedret Billor

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Independent component analysis (ICA) is one of the most commonly used blind source separation (BSS) techniques for signal pre-processing, such as noise reduction and feature extraction. The main parameter in the ICA method is the number of independent components (IC). Although there have been several methods for the determination of the number of ICs, it has not been given sufficient attentionto this important parameter. In this study, wereview the mostused methods fordetermining the number of ICs and providetheir advantages and disadvantages. Further, wepropose an improved version of column-wise ICAByBlock method for the determination of the number of ICs.To assess the performance of the proposed method, we compare the column-wise ICAbyBlock with several existing methods through different ICA methods by using simulated and real signal data. Results show that the proposed column-wise ICAbyBlock is an effective and stable method for determining the optimal number of components in ICA. This method is simple, and results can be demonstrated intuitively with good visualizations.

Keywords: independent component analysis, optimal number, column-wise, correlation coefficient, cross-validation, ICAByblock

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Magdalena Idzikowska, Katarzyna Pająk, Kamil Kowalczyk

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Seafloor topography is a fundamental issue in geological, geophysical, and oceanographic studies. Single-beam or multibeam sonars attached to the hulls of ships are used to emit a hydroacoustic signal from transducers and reproduce the topography of the seabed. This solution provides relevant accuracy and spatial resolution. Bathymetric data from ships surveys provides National Centers for Environmental Information – National Oceanic and Atmospheric Administration. Unfortunately, most of the seabed is still unidentified, as there are still many gaps to be explored between ship survey tracks. Moreover, such measurements are very expensive and time-consuming. The solution is raster bathymetric models shared by The General Bathymetric Chart of the Oceans. The offered products are a compilation of different sets of data - raw or processed. Indirect data for the development of bathymetric models are also measurements of gravity anomalies. Some forms of seafloor relief (e.g. seamounts) increase the force of the Earth's pull, leading to changes in the sea surface. Based on satellite altimetry data, Sea Surface Height and marine gravity anomalies can be estimated, and based on the anomalies, it’s possible to infer the structure of the seabed. The main goal of the work is to create regional bathymetric models and models of the sea surface in the area of the east coast of North America – a region of seamounts and undulating seafloor. The research includes an analysis of the methods and techniques used, an evaluation of the interpolation algorithms used, model thickening, and the creation of grid models. Obtained data are raster bathymetric models in NetCDF format, survey data from multibeam soundings in MB-System format, and satellite altimetry data from Copernicus Marine Environment Monitoring Service. The methodology includes data extraction, processing, mapping, and spatial analysis. Visualization of the obtained results was carried out with Geographic Information System tools. The result is an extension of the state of the knowledge of the quality and usefulness of the data used for seabed and sea surface modeling and knowledge of the accuracy of the generated models. Sea level is averaged over time and space (excluding waves, tides, etc.). Its changes, along with knowledge of the topography of the ocean floor - inform us indirectly about the volume of the entire water ocean. The true shape of the ocean surface is further varied by such phenomena as tides, differences in atmospheric pressure, wind systems, thermal expansion of water, or phases of ocean circulation. Depending on the location of the point, the higher the depth, the lower the trend of sea level change. Studies show that combining data sets, from different sources, with different accuracies can affect the quality of sea surface and seafloor topography models.

Keywords: seafloor, sea surface height, bathymetry, satellite altimetry

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Authors: V. Lokesh, M. Manjunathan, S. Sairam, K. Saithsh Kumar, R. Anantharaj

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The densities of pure and its binary mixtures of 1-Butyl-3-methyl imidazolium bis (trifluoro methyl sulfonyl) imide and 1–Ethyl-3-methyl imidazolium ethyl sulphate at different temperature, over the entire composition range were measured at 293.15, 298.15, 303.15, 308.15, 313.15, 318.15, 323.15, 328.15, 33.15, 338.15, 343.15 K. In this study, the liquid-liquid extraction procedure was used. From this experimental data, the excess molar volumes, apparent molar volume, partial molar volumes and the excess partial molar volumes have been calculated for over the whole composition range. Hence, the effect of temperature and composition on all derived thermodynamic properties of this binary mixture will be discussed in terms of intermolecular interactions.

Keywords: ionic liquid, interaction energy, effect of temperature, effect of composition

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Authors: N. Prabavathy, R. Balasundaraprabhu, S. Shalini, Dhayalan Velauthapillai, S. Prasanna, N. Muthukumarasamy

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Dye sensitized solar cell (DSSC) has become a significant research area due to their fundamental and scientific importance in the area of energy conversion. Synthetic dyes as sensitizer in DSSC are efficient and durable but they are costlier, toxic and have the tendency to degrade. Natural sensitizers contain plant pigments such as anthocyanin, carotenoid, flavonoid, and chlorophyll which promote light absorption as well as injection of charges to the conduction band of TiO2 through the sensitizer. But, the efficiency of natural dyes is not up to the mark mainly due to instability of the pigment such as anthocyanin. The stability issues in vitro are mainly due to the effect of solvents on extraction of anthocyanins and their respective pH. Taking this factor into consideration, in the present work, the anthocyanins were extracted from the flower Caesalpinia pulcherrima (C. pulcherrimma) with various solvents and their respective stability and pH values are discussed. The usage of citric acid as solvent to extract anthocyanin has shown good stability than other solvents. It also helps in enhancing the sensitization properties of anthocyanins with Titanium dioxide (TiO2) nanorods. The IPCE spectra show higher photovoltaic performance for dye sensitized TiO2nanorods using citric acid as solvent. The natural DSSC using citric acid as solvent shows a higher efficiency compared to other solvents. Hence citric acid performs to be a safe solvent for natural DSSC in boosting the photovoltaic performance and maintaining the stability of anthocyanins.

Keywords: Caesalpinia pulcherrima, citric acid, dye sensitized solar cells, TiO₂ nanorods

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Authors: Bouharati Lokman, Bouharati Imen, Bouharati Khaoula, Bouharati Oussama, Bouharati Saddek

Abstract:

Ionizing radiation exposure is an established cancer risk factor. Compared to other common environmental carcinogens, it is relatively easy to determine organ-specific radiation dose and, as a result, radiation dose-response relationships tend to be highly quantified. Nevertheless, there can be considerable uncertainty about questions of radiation-related cancer risk as they apply to risk protection and public policy, and the interpretations of interested parties can differ from one person to another. Examples of tools used in the analysis of the risk of developing cancer due to radiation are characterized by uncertainty. These uncertainties are related to the history of exposure and different assumptions involved in the calculation. We believe that the results of statistical calculations are characterized by uncertainty and imprecision. Having regard to the physiological variation from one person to another. In this study, we develop a tool based on fuzzy logic inference. As fuzzy logic deals with imprecise and uncertain, its application in this area is adequate. We propose a fuzzy system with three input variables (age, sex and body attainable cancer). The output variable expresses the risk of infringement rate of each organ. A base rule is established from recorded actual data. After successful simulation, this will instantly predict the risk of infringement rate of each body following chronic exposure to 0.1 Gy.

Keywords: radiation exposure, cancer, modeling, fuzzy logic

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Authors: Zarina Chokparova, Ighor Uzhinsky

Abstract:

Carbon dioxide emissions resulting from burning of the fossil fuels on large scales, such as oil industry or power plants, leads to a plenty of severe implications including global temperature raise, air pollution and other adverse impacts on the environment. Besides some precarious and costly ways for the alleviation of CO₂ emissions detriment in industrial scales (such as liquefaction of CO₂ and its deep-water treatment, application of adsorbents and membranes, which require careful consideration of drawback effects and their mitigation), one physically and commercially available technology for its capture and disposal is supersonic system for inertial extraction of CO₂ in after-combustion streams. Due to the flue gas with a carbon dioxide concentration of 10-15 volume percent being emitted from the combustion system, the waste stream represents a rather diluted condition at low pressure. The supersonic system induces a flue gas mixture stream to expand using a converge-and-diverge operating nozzle; the flow velocity increases to the supersonic ranges resulting in rapid drop of temperature and pressure. Thus, conversion of potential energy into the kinetic power causes a desublimation of CO₂. Solidified carbon dioxide can be sent to the separate vessel for further disposal. The major advantages of the current solution are its economic efficiency, physical stability, and compactness of the system, as well as needlessness of addition any chemical media. However, there are several challenges yet to be regarded to optimize the system: the way for increasing the size of separated CO₂ particles (as they are represented on a micrometers scale of effective diameter), reduction of the concomitant gas separated together with carbon dioxide and provision of CO₂ downstream flow purity. Moreover, determination of thermodynamic conditions of the vapor-solid mixture including specification of the valid and accurate equation of state remains to be an essential goal. Due to high speeds and temperatures reached during the process, the influence of the emitted heat should be considered, and the applicable solution model for the compressible flow need to be determined. In this report, a brief overview of the current technology status will be presented and a program for further evaluation of this approach is going to be proposed.

Keywords: CO₂ sequestration, converging diverging nozzle, fossil fuel power plant emissions, inertial CO₂ extraction, supersonic post-combustion carbon dioxide capture

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Authors: Chi-Feng Lai, Wen-Chun Liao Liao

Abstract:

Many people are bothered by sleep difficulties in Taiwan’s society. However, majority of patients get medical treatments without a comprehensive sleep assessment. It is still a big challenge to formulate a comprehensive assessment of sleep difficulties in clinical settings, even though many assessment tools have existed in literature. This study tries to implement reliable and effective ‘comprehensive sleep assessment scales’ in a medical center and to explore differences in sleep-related factors between clinic clients with or without sleep difficulty complaints. The comprehensive sleep assessment (CSA) scales were composed of 5 dimensions: ‘personal factors’, ‘physiological factors’, ‘psychological factors’, ‘social factors’ and ‘environmental factors, and were first evaluated by expert validity and 20 participants with test-retest reliability. The Content Validity Index (CVI) of the CSA was 0.94 and the alpha of the consistency reliability ranged 0.996-1.000. Clients who visited sleep clinic due to sleep difficulties (n=32, 16 males and 16 females, ages 43.66 ±14.214) and gender-and age- matched healthy subjects without sleep difficulties (n=96, 47 males and 49 females, ages 41.99 ±13.69) were randomly recruited at a ratio of 1:3 (with sleep difficulties vs. without sleep difficulties) to compare their sleep and the CSA factors. Results show that all clinic clients with sleep difficulties did have poor sleep quality (PSQI>5) and mild to moderate daytime sleepiness (ESS >11). Personal factors of long working hours (χ2= 10.315, p=0.001), shift workers (χ2= 8.964, p=0.003), night shift (χ2=9.395, p=0.004) and perceived stress (χ2=9.503, p=0.002) were disruptors of sleep difficulties. Physiological factors from physical examination including breathing by mouth, low soft palate, high narrow palate, Edward Angle, tongue hypertrophy, and occlusion of the worn surface were observed in clinic clients. Psychological factors including higher perceived stress (χ2=32.542, p=0.000), anxiety and depression (χ2=32.868, p=0.000); social factors including lack of leisure activities (χ2=39.857, p=0.000), more drinking habits (χ2=1.798, p=0.018), irregular amount and frequency in meals (χ2=5.086, p=0.024), excessive dinner (χ2=21.511, p=0.000), being incapable of getting up on time due to previous poor night sleep (χ2=4.444, p=0.035); and environmental factors including lights (χ2=7.683, p=0.006), noise (χ2=5.086, p=0.024), low or high bedroom temperature (χ2=4.595, p=0.032) were existed in clients. In conclusion, the CSA scales can work as valid and reliable instruments for evaluating sleep-related factors. Findings of this study provide important reference for assessing clinic clients with sleep difficulties.

Keywords: comprehensive sleep assessment, sleep-related factors, sleep difficulties

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

Abstract:

Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: M. Goliszek, M. Sobiesiak, O. Sevastyanova, B. Podkoscielna

Abstract:

Lignin is one of three main constituents of biomass together with cellulose and hemicellulose. It is a complex biopolymer, which contains a large number of functional groups, including aliphatic and aromatic hydroxyl groups, carbohylic groups and methoxy groups in its structure, that is why it shows potential capacities for process of sorption. Lignin is a highly cross-linked polymer with a three-dimentional structure which can provide large surface area and pore volumes. It can also posses better dispersion, diffusion and mass transfer behavior in a field of the removal of, e.g., heavy-metal-ions or aromatic pollutions. In this work emulsion-suspension copolymerization method, to synthesize the porous microspheres of divinylbenzene (DVB), styrene (St) and lignin was used. There are also microspheres without the addition of lignin for comparison. Before the copolymerization, modification lignin with methacryloyl chloride, to improve its reactivity with other monomers was done. The physico-chemical properties of the obtained microspheres, e.g., pore structures (adsorption-desorption measurements), thermal properties (DSC), tendencies to swell and the actual shapes were also studied. Due to well-developed porous structure and the presence of functional groups our materials may have great potential in sorption processes. To estimate the sorption capabilities of the microspheres towards phenol and its chlorinated derivatives the off-line SPE (solid-phase extraction) method is going to be applied. This method has various advantages, including low-cost, easy to use and enables the rapid measurements for a large number of chemicals. The efficiency of the materials in removing phenols from aqueous solution and in desorption processes will be evaluated.

Keywords: microspheres, lignin, sorption, solid-phase extraction

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Authors: Chellammal Surianarayanan

Abstract:

Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

Procedia PDF Downloads 403