Search results for: neural style transfer

Authors: V. A. Ritchie

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The loss of life and livelihood experienced after the magnitude 9.3 Sumatra earthquake and tsunami on 26 December 2004 and magnitude 9 earthquake and tsunami in northeastern Japan on 11 March 2011, has raised global awareness and brought tsunami phenomenology, nomenclature, and representation into sharp focus. At the same time, travel and tourism continue to increase, contributing around 1 in 11 jobs worldwide. This increase in tourism is especially true for coastal zones, placing pressure on decision-makers to downplay tsunami risks and at the same time provide adequate tsunami warning so that holidaymakers will feel confident enough to visit places of high tsunami risk. This study investigates how well tsunami preparedness messages are getting through for tourist-based businesses in Hawke’s Bay New Zealand, a region of frequent seismic activity and a high probability of experiencing a nearshore tsunami. The aim of this study is to investigate whether tourists based businesses are well informed about tsunamis, how well they understand that information and to what extent their clients are included in awareness raising and evacuation processes. In high-risk tsunami zones, such as Hawke’s Bay, tourism based businesses face competitive tension between short term business profitability and longer term reputational issues related to preventable loss of life from natural hazards, such as tsunamis. This study will address ways to accommodate culturally and linguistically relevant tourist awareness measures without discouraging tourists or being too costly to implement.

Keywords: tsunami risk and response, travel and tourism, business preparedness, cross cultural knowledge transfer

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Authors: Farshid Fathinia

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This paper examines numerically the laminar steady magneto-hydrodynamic mixed convection flow and heat transfer in a wavy lid-driven cavity filled with water-Al2O3 nanofluid using FDM method. The left and right sidewalls of the cavity have a wavy geometry and are maintained at a cold and hot temperature, respectively. The top and bottom walls are considered flat and insulated while, the bottom wall moves from left to right direction with a uniform lid-driven velocity. A magnetic field is applied vertically downward on the bottom wall of the cavity. Based on the numerical results, the effects of the dominant parameters such as Rayleigh number, Hartmann number, solid volume fraction, and wavy wall geometry parameters are examined. The numerical results are obtained for Hartmann number varying as 0 ≤ Ha ≤ 0.6, Rayleigh numbers varying as 103≤ Ra ≤105, and the solid volume fractions varying as 0 ≤ φ ≤ 0.0003. Comparisons with previously published numerical works on mixed convection in a nanofluid filled cavity are performed and good agreements between the results are observed. It is found that the flow circulation and mean Nusselt number decrease as the solid volume fraction and Hartmann number increase. Moreover, the convection enhances when the amplitude ratio of the wavy surface increases. The results also show that both the flow and thermal fields are significantly affected by the amplitude ratio (i.e., wave form) of the wavy wall.

Keywords: nanofluid, mixed convection, magnetic field, wavy cavity, lid-driven, SPH method

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Authors: Veronika Lesáková, Silvia Slezáková, František Štěpánek

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Drug dosage forms consisting of multi-unit particle systems (MUPS) for modified drug release provide a promising route for overcoming the limitation of conventional tablets. Despite the conventional use of pellets as units for MUP systems, 3D printed polymers loaded with a drug seem like an interesting candidate due to the control over dosing that 3D printing mechanisms offer. Further, 3D printing offers high flexibility and control over the spatial structuring of a printed object. The final MUPS tablets include PVP and HPC as granulate with other excipients, enabling the compaction process of this mixture with 3D printed inclusions, also termed minitablets. In this study, we have developed the multi-step production process for MUPS tablets, including the 3D printing technology. The MUPS tablets with incorporated 3D printed minitablets are a complex system for drug delivery, providing modified drug release. Such structured tablets promise to reduce drug fluctuations in blood, risk of local toxicity, and increase bioavailability, resulting in an improved therapeutic effect due to the fast transfer into the small intestine, where particles are evenly distributed. Drug loaded 3D printed minitablets were compacted into the excipient mixture, influencing drug release through varying parameters, such as minitablets size, matrix composition, and compaction parameters. Further, the mechanical properties and morphology of the final MUPS tablets were analyzed as many properties, such as plasticity and elasticity, can significantly influence the dissolution profile of the drug.

Keywords: 3D printing, dissolution kinetics, drug delivery, hot-melt extrusion

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Authors: Tatsiana Haiden

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The role of the translator in the publishing process has been underestimated for centuries. Any translation is produced in a certain socio-political context by agents with different background, interests, and opinions, i.e., no translation is neutral. Any translation goes beyond the text; it is not only an interlingual transfer of signs but a social phenomenon. The case study shows how Jewish social networks influence publishing translations and aims to explain the unexpected success of the Jewish publishing house in pre-Anschluss Austria. The research shows that translators play a central role (‘Translator’s visibility’ - Pym, ‘Activist turn’ - Wolf, ‘Translator studies’ - Chesterman) in choosing what has to be translated and establishing communication between the author and the publisher. The concept of Translationskultur of Prunc is being historized and applied to the publishing house for the first time by analyzing business correspondence between the main actors of translation (publisher-translator-author). The translation studies project has become interdisciplinary –it encompasses sociology (concepts of Bourdieu’s ‘Field theory’ are used) and history. The historical research method Histoire croiseé is being used to avoid subjectivity and to introduce a new ‘translator-oriented’ vision in translation studies instead of the author-oriented one. In the course of the archival research, it was established that Jewish background plays an essential role in the destiny of the translators and the publishing house, so the Jewish studies have been added to the project. The study goes beyond the Austrian translational culture; it can be used as an example of dealing with publishing houses policies, publishing translations, and translator studies.

Keywords: history of translation, Jewish studies, publishing translations, translation sociology, translator studies, translators as actors

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Authors: Stefana Francisca Lupu, Jasmin Chantah, Mara Krone, Ingo Roden, Stephan Bongard, Gunter Kreutz

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Long-term music interventions could enhance both musical and nonmusical skills. The present study was designed to explore cognitive, socio-emotional, and musical development in a longitudinal setting. Third-graders (N = 184: 87 male, 97 female; mean age = 8.61 years; 115 native German and 69 migrant children) were randomly assigned to two intervention groups (music and maths) and a control group over a period of one school-year. At baseline, children in these groups were similar in basic cognitive skills, with a trend of advantage in the control group. Dependent measures included the culture fair intelligence test CFT 20-R; the questionnaire of emotional and social school experience for grade 3 and 4 (FEESS 3-4), the test of resources in childhood and adolescence (FRKJ 8-16), the test of language proficiency for German native and non-native primary school children (SFD 3), the reading comprehension test (ELFE 1-6), the German math test (DEMAT 3+) and the intermediate measures of music audiation (IMMA). Data were collected two times at the beginning (T1) and at the end of the school year (T2). A third measurement (T3) followed after a six months retention period. Data from baseline and post-intervention measurements are currently being analyzed. Preliminary results of all three measurements will be presented at the conference.

Keywords: musical training, primary-school German and migrant children, socio-emotional skills, transfer

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Authors: Usman Jilani, Ibad Khurram, Irshad Hussain

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Environmentally sustainable waste management is a challenging task as it involves multiple and diverse economic, environmental, technical and regulatory issues. Municipal Solid Waste Management (MSWM) is more challenging in developing countries like Pakistan due to lack of awareness, technology and human resources, insufficient funding, inefficient collection and transport mechanism resulting in the lack of a comprehensive waste management system. This work presents an overview of current MSWM practices in Peshawar, the provincial capital of Khyber Pakhtunkhwa, Pakistan and proposes a better and sustainable integrated solid waste management system with incineration (Waste to Energy) option. The diverted waste would otherwise generate revenue; minimize land fill requirement and negative impact on the environment. The proposed optimized solution utilizing scientific techniques (like mathematical modeling, optimization algorithms and GIS) as decision support tools enhances the technical & institutional efficiency leading towards a more sustainable waste management system through incorporating: - Improved collection mechanisms through optimized transportation / routing and, - Resource recovery through incineration and selection of most feasible sites for transfer stations, landfills and incineration plant. These proposed methods shift the linear waste management system towards a cyclic system and can also be used as a decision support tool by the WSSP (Water and Sanitation Services Peshawar), agency responsible for the MSWM in Peshawar.

Keywords: municipal solid waste management, incineration, mathematical modeling, optimization, GIS, Peshawar

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Authors: Tatjana Stefanovic Stanojevic, Milica Tosic Radev, Aleksandra Bogdanovic

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Depression is considered to be a leading cause of death and disability in the female population, and that is the reason why understanding the dynamics of the onset of depressive symptomatology is important. A review of the literature indicates the relationship between depressive symptoms and insecure attachment patterns, but very few studies have examined the mechanism underlying this relation. The aim of the study was to examine the pathway from the preoccupied attachment pattern to depressive symptomatology, as well as to test the mediation effect of mentalization, social anxiety and rumination in this relationship using a serial mediation model. The research was carried out on a geographical cluster sample from the general population of Serbia included within the project ‘Indicators and models of family and work roles harmonization’ funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia. This research was carried out on a subsample of 791 working-age female adults from 37 urban and rural locations distributed through 20 administrative districts of Serbia. The respondents filled in a battery of instruments, including Relationship Questionnaire - Clinical Version (RQ - CV), The Mentalization Scale (MentS), Scale of Social Anxiety (SA), Patient Ruminative Thought Style Questionnaire (RTSQ), Health Questionnaire (PHQ-9). The results confirm our assumption that the total indirect effect of the preoccupied attachment pattern to depressive symptoms is significant across all mediators separately. More importantly, this effect is still present in a model with a sequential mediator relationship, where social anxiety, rumination, and mentalization were perceived as serial mediators of a relationship between preoccupied attachment and depressive symptoms (estimated indirect effect=0.004, boot-strapped 95% CI=0.002 to 0.007). Our findings suggest that there is a significant specific indirect effect of the preoccupied attachment pattern to depressive symptoms, occurring through mentalization, social anxiety and rumination, indicating that preoccupied attachment cause decrease of a self related mentalization, which in turn causes increasing of social anxiety and rumination, concluding in depressive symptoms as a final consequence. The finding that the path from the preoccupied attachment pattern to depressive symptoms is typical in women is understandable from the perspective of both evolutionary and culturally conditioned gender differences. The practical implications of the study are reflected in the recommendations for the prevention and forehand psychotherapy response among preoccupied women with depressive symptomatology. Treatment of this specific group of depressed patients should be focused on strengthening mentalization, learning to accept and to understand herself better, reducing anxiety in situations where mistakes are visible to others, and replacing the rumination strategy with more constructive coping strategies.

Keywords: preoccupied attachment, depression, serial mediation model, mentalization, rumination

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Authors: Juan Patricio Ibáñez, Exequiel López

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A method for synthesizing copper nanoparticles through an electrochemical approach is proposed, employing surfactants to stabilize the size of the newly formed nanoparticles. The electrolyte was made up of a matrix of H₂SO₄ (190 g/L) having Cu²⁺ (from 3.2 to 9.5 g/L), sodium dodecyl sulfate -SDS- (from 0.5 to 1.0 g/L) and Tween 80 (from 0 to 7.5 mL/L). Tween 80 was used in a molar relation of 1 to 1 with SDS. A glass cell was used, which was in a thermostatic water bath to keep the system temperature, and the electrodes were cathodic copper as an anode and stainless steel 316-L as a cathode. This process was influenced by the control exerted through the initial copper concentration in the electrolyte and the applied current density. Copper nanoparticles of electrolytic purity, exhibiting a spherical morphology of varying sizes with low dispersion, were successfully produced, contingent upon the chemical composition of the electrolyte and current density. The minimum size achieved was 3.0 nm ± 0.9 nm, with an average standard deviation of 2.2 nm throughout the entire process. The deposited copper mass ranged from 0.394 g to 1.848 g per hour (over an area of 25 cm²), accompanied by an average Faradaic efficiency of 30.8% and an average specific energy consumption of 4.4 kWh/kg. The chemical analysis of the product employed X-ray powder diffraction (XRD), while physical characteristics such as size and morphology were assessed using atomic force microscopy (AFM). It was identified that the initial concentration of copper and the current density are the variables defining the size and dispersion of the nanoparticles, as they serve as reactants in the cathodic half-reaction. The presence of surfactants stabilizes the nanoparticle size as their molecules adsorb onto the nanoparticle surface, forming a thick barrier that prevents mass transfer with the exterior and halts further growth.

Keywords: copper nanopowder, electrochemical synthesis, current density, surfactant stabilizer

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Authors: Tetsya Sok, Seong Jae Hong, Young Kyu Kim, Seung Woo Lee

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The temperature distribution for asphalt concrete (AC)-Concrete composite pavement is one of main influencing factor that affects to performance life of pavement. The temperature gradient in concrete slab underneath the AC layer results the critical curling stress and lead to causes de-bonding of AC-Concrete interface. These stresses, when enhanced by repetitive axial loadings, also contribute to the fatigue damage and eventual crack development within the slab. Moreover, the temperature change within concrete slab extremely causes the slab contracts and expands that significantly induces reflective cracking in AC layer. In this paper, the numerical prediction of pavement temperature was investigated using one-dimensional finite different method (FDM) in fully explicit scheme. The numerical predicted model provides a fundamental and clear understanding of heat energy balance including incoming and outgoing thermal energies in addition to dissipated heat in the system. By using the reliable meteorological data for daily air temperature, solar radiation, wind speech and variable pavement surface properties, the predicted pavement temperature profile was validated with the field measured data. Additionally, the effects of AC thickness and daily air temperature on the temperature profile in underlying concrete were also investigated. Based on obtained results, the numerical predicted temperature of AC-Concrete composite pavement using FDM provided a good accuracy compared to field measured data and thicker AC layer significantly insulates the temperature distribution in underlying concrete slab.

Keywords: asphalt concrete, finite different method (FDM), curling effect, heat transfer, solar radiation

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Authors: Umit Cali

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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Mohd Ali Abbas Zaidi, Meenu Sachdeva, Jonaki Sen

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In the vertebrate embryo, the forebrain anlagen develops from the anterior-most region of the neural tube which is the precursor of the central nervous system (CNS). The roof plate located at the dorsal midline region of the forebrain anlagen, acts as a source of several secreted molecules involved in patterning and morphogenesis of the forebrain. One such key morphogenetic event is the invagination of the forebrain roof plate which results in separation of the single forebrain vesicle into two cerebral hemispheres. Retinoic acid (RA) signaling plays a key role in this process. Blocking RA signaling at the dorsal forebrain midline inhibits dorsal invagination and results in the absence of certain key features of this region, such as thinning of the neuroepithelium and a lowering of cell proliferation. At present we are investigating the possibility of other signaling pathways acting in concert with RA signaling to regulate this process. We have focused on BMP signaling, which we found to be active in a mutually exclusive domain to that of RA signaling within the roof plate. We have also observed that there is a change in BMP signaling activity on modulation of RA signaling indicating an antagonistic relationship between the two. Moreover, constitutive activation of BMP signaling seems to completely inhibit thinning and partially affect invagination, leaving the lowering of cell proliferation in the midline unaffected. We are employing in-silico modeling as well as molecular manipulations to investigate the relative contribution if any, of regional differences in rates of cell proliferation and thinning of the neuroepithelium towards the process of invagination. We have found expression of certain cell adhesion molecules in forebrain roof-plate whose mRNA localization across the thickness of neuroepithelium is influenced by Bmp and RA signaling, giving regional rigidity to roof plate and assisting invagination. We also found expression of certain cytoskeleton modifiers in a localized small domains in invaginating forebrain roof plate suggesting that midline invagination is under control of many factors.

Keywords: bone morphogenetic signaling, cytoskeleton, cell adhesion molecules, forebrain roof plate, retinoic acid signaling

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Authors: An-Na Li, Ying-Yu Chen, Yu-Lung Lin

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Food tourism is one of the growing industries in the tourism industry today. The Destination Marketing Organizations (DMOs) are aware of the importance of gastronomy to stimulate local and regional economic development. From the heritage and cultural aspects, gastronomy is becoming a more important part of the cultural heritage of the region. Heritage destinations provide culinary heritage, which fits the current interest in traditional food, and cuisine is a part of a general desire for authentic experience. However, few studies have empirically examined antecedents of food tourists’ behavioral intentions. This study examined the effects of cuisine experience; emotions, place attachment and tourists’ revisit behavioral intentions. A total of 408 individuals responded to the on-site survey in the historic town of Lu-Kang in Taiwan. The results indicated that tourists’ cuisine experience include place flavor, media recommendation, local learning, life transfer and interpersonal share. In addition, cuisine experience had significant impacts on emotions and place attachment, emotions had significant effects on place attachment, furthermore, which in turn place attachment had significant effects on tourists’ revisit behavioral intentions. The findings suggested that the cuisine experience is a multi-dimensions construct. On the other hands, the good quality of cuisine experience could evoke tourists’ positive emotions and it could play a significant role in promoting tourist revisit intentions or word of mouth. Implications for theory and practice are discussed.

Keywords: culinary tourism, cuisine experiences, emotions, revisit intentions

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Authors: S. Jalilzadeh, S. M. Mohseni Bonab

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Solar and battery energy storage systems are very useful for consumers who live in deprived areas and do not have access to electricity distribution networks. Nowadays one of the problems that photo voltaic systems (PV) have changing of output power in temperature and irradiance variations, which directly affects the load that is connected to photo voltaic systems. In this paper, with considering the fact that the solar array varies with change in temperature and solar power radiation, a voltage stabilizer system of a load connected to photo voltaic array is designed to stabilize the load voltage and to transfer surplus power of the battery. Also, in proposed hybrid system, the needed load power amount is supplemented considering the voltage stabilization in standalone operation for supplying unbalanced AC load. Electrical energy storage system for voltage control and improvement of the performance of PV by a DC/DC converter is connected to the DC bus. The load is also feed by an AC/DC converter. In this paper, when the voltage increases in its reference limit, the battery gets charged by the photo voltaic array and when it decreases in its defined limit, the power gets injected to the DC bus by this battery. The constant of DC bus Voltage is the cause for the reduced harmonics generated by the inverter. In addition, a series of filters are provided in the inverter output in to reduced harmonics. The inverter control circuit is designed that the voltage and frequency of the load remain almost constant at different load conditions. This paper has focused on controlling strategies of converters to improve their performance.

Keywords: photovoltaic array (PV), DC/DC Boost converter, battery converter, inverters control

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: I. Hilia, C. Hange, F. Hakala, M. Matheus, C. Jansen, J. Hidinwa, O. Awofolu

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The article reports on the occurrences, level, and mobility of selected trace metals in environmental samples. The conceptual basis was to examine the possible influence of anthropogenic activities and the impact on human and environmental health. Environmental samples (soil, plant and lower animal) were randomly collected from stratified study/sampling areas, preserved and pre-treated before analysis. Mineral acid digestion procedure was employed for the isolation of metallic contents in samples, and elemental qualitative and quantitative analysis was by ICP-OES. Analytical protocol was validated through the quality assurance process and was found acceptable with quantitative metallic recoveries in the range of 85-90%; hence considered applicable for the analyses of environmental samples. The mean concentration of analysed metals in soil samples ranged from 53.2- 2532.8 mg/kg (Cu); 59.5- 2020.1 mg/kg (Zn); 1.80 – 21.26 mg/kg (Cd) and 19.6- 140.9 mg/kg (Pb). The mean level in grass samples ranged from 9.33 – 38.63 mg/kg (Cu); 64.20-105.18 mg/kg (Zn); 0.28–0.73 mg/kg (Cd) and 0.53 -16.26 mg/kg (Pb) while the mean level in lower animal sample (beetle) varied from 9.6 - 105.3 mg/kg (Cu); 134.1-297.2 mg/kg (Zn); 0.63 – 3.78 (Cd) and 8.0 – 29.1 mg/kg (Pb) across sample collection points (SCPs) 1-4 respectively. Metallic transfer factors (TFs) were in the order Zn >Cd > Cu > Pb with metal Pollution Indices (MPIs) in the order SCP1 > SCP2 > SCP3 > SCP4. About 60-70 % of analysed metals were above the maximum allowable limits (MALs) in soil and plant samples. Results obtained revealed the general prevalence of analysed metals at all sampled sites with indication of metallic mobility across the food chain which portrayed dire consequences for environmental and human health. Systematic environmental remediation and pollution abatement strategies are recommended.

Keywords: trace metals, pollution, human health, Incidences, ICP-OES

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Authors: Abhijit Trailokya

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Background: Cardiovascular diseases (CVD’s) are the major cause of morbidity and mortality in both developed and developing countries. Many clinical trials have demonstrated that low-density lipoprotein cholesterol (LDL-C) lowering, reduces the incidence of coronary and cerebrovascular events across a broad spectrum of patients at risk. Guidelines for the management of patients at risk have been established in Europe and North America. The guidelines have advocated progressively lower LDL-C targets and more aggressive use of statin therapy. In Indian patients, comprehensive data on dyslipidemia management and its treatment outcomes are inadequate. There is lack of information on existing treatment patterns, the patient’s profile being treated, and factors that determine treatment success or failure in achieving desired goals. Purpose: The present study was planned to determine the lipid control status in high-risk dyslipidemic patients treated with lipid-lowering therapy in India. Methods: This cross-sectional, non-interventional, single visit program was conducted across 483 sites in India where male and female patients with high-risk dyslipidemia aged 18 to 65 years who had visited for a routine health check-up to their respective physician at hospital or a healthcare center. Percentage of high-risk dyslipidemic patients achieving adequate LDL-C level (< 70 mg/dL) on lipid-lowering therapy and the association of lipid parameters with patient characteristics, comorbid conditions, and lipid lowering drugs were analysed. Results: 3089 patients were enrolled in the study; of which 64% were males. LDL-C data was available for 95.2% of the patients; only 7.7% of these patients achieved LDL-C levels < 70 mg/dL on lipid-lowering therapy, which may be due to inability to follow therapeutic plans, poor compliance, or inadequate counselling by physician. The physician’s lack of awareness about recent treatment guidelines also might contribute to patients’ poor adherence, not explaining adequately the benefit and risks of a medication, not giving consideration to the patient’s life style and the cost of medication. Statin was the most commonly used anti-dyslipidemic drug across population. The higher proportion of patients had the comorbid condition of CVD and diabetes mellitus across all dyslipidemic patients. Conclusion: As per the European Society of Cardiology guidelines the ideal LDL-C levels in high risk dyslipidemic patients should be less than 70%. In the present study, 7.7% of the patients achieved LDL-C levels < 70 mg/dL on lipid lowering therapy which is very less. Most of high risk dyslipidemic patients in India are on suboptimal dosage of statin. So more aggressive and high dosage statin therapy may be required to achieve target LDLC levels in high risk Indian dyslipidemic patients.

Keywords: cardiovascular disease, diabetes mellitus, dyslipidemia, LDL-C, lipid lowering drug, statins

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Authors: Sudhaveni Naresh

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International migration is an important subject and gaining more significance andinterest among scholars in recent years. It is defined as crossing of the boundaries of political or administrative units for a certain minimum period for reasons such as education, employment, etc.International migration is not new for India because it has a long history with the Gulf region since ancient period. India is also one of the largest migrant-sending countries after China in the world. Migration towards the Gulf region became more prominent during early 1970s due to oil boom which led to rapid increase in the demand for foreign labour. Of 25 million Indian emigrants are living across the world, about six million Indian emigrants working in the Gulf. Most of these migrants were either unskilled or semi-skilled. Both the pull and push factors behind labour emigrate to Gulf countries. India is world’s leading receiver of remittances and the flow of remittances to India has been increasing steadily since the 1970s. In 2011-12, it was about 4 percent of GDP.Emigrants play a significant role in the economic development and growth of the country via the remittances and knowledge and skill transfer. Scholars see remittances as vital tool in the development for origin country. This paper examines the recent trend and pattern of migration from India to Gulf countries and explores impact of remittances on emigrants’ families at home country. It also highlights opportunities, challenges and the need for strengthening multilateral cooperation to transform migration into an efficient, orderly and humane process.The study propose to undertake a primary survey for this purpose. Both quantitative and qualitative research methods will be used to study the above issues.

Keywords: development, international migration, remittances, unskilled labour

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Authors: Nurliani, Ida Rosada

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The national rice consumption keeps increasing along with raising income of the households and the rapid growth of population. However, food availability, particularly rice, is limited. Impacts of rice-field conversion have run cumulatively, as we can see on potential losses of rice and crops production, as well as work opportunity that keeps increasing year-by-year. Therefore, it requires policy recommendation to control rice-field conversion through economic, social, and ecological approaches. The research was a survey method intended to: (1) Identify internal factors; quality and productivity of the land as the cause of land conversion, (2) Identify external factors of land conversion, value of the rice-field and the competitor’s land, workforce absorption, and regulation, as well as (3) Formulate strategies in controlling rice-field conversion. Population of the research was farmers who applied land conversion at Pangkep Regency, South Sulawesi. Samples were determined using the incidental sampling method. Data analysis used productivity analysis, land quality analysis, total economic value analysis, and SWOT analysis. Results of the research showed that the quality of rice-field was low as well as productivity of the grains (unhulled-rice). So that, average productivity of the grains and quality of rice-field were low as well. Total economic value of rice-field was lower than the economic value of the embankment. Workforce absorption value on rice-field was higher than on the embankment. Strategies in controlling such rice-field conversion can be done by increasing rice-field productivity, improving land quality, applying cultivation technique of specific location, improving the irrigation lines, and socializing regulation and sanction about the transfer of land use.

Keywords: land conversion, quality of rice-field, productivity, land economic value.

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Authors: Alia Alghosoun, Ashraf Osman, Mohammed Seaid

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A coupled two-layer finite volume/finite element method was proposed for solving dam-break flow problem over deformable beds. The governing equations consist of the well-balanced two-layer shallow water equations for the water flow and a linear elastic model for the bed deformations. Deformations in the topography can be caused by a brutal localized force or simply by a class of sliding displacements on the bathymetry. This deformation in the bed is a source of perturbations, on the water surface generating water waves which propagate with different amplitudes and frequencies. Coupling conditions at the interface are also investigated in the current study and two mesh procedure is proposed for the transfer of information through the interface. In the present work a new procedure is implemented at the soil-water interface using the finite element and two-layer finite volume meshes with a conservative distribution of the forces at their intersections. The finite element method employs quadratic elements in an unstructured triangular mesh and the finite volume method uses the Rusanove to reconstruct the numerical fluxes. The numerical coupled method is highly efficient, accurate, well balanced, and it can handle complex geometries as well as rapidly varying flows. Numerical results are presented for several test examples of dam-break flows over deformable beds. Mesh convergence study is performed for both methods, the overall model provides new insight into the problems at minimal computational cost.

Keywords: dam-break flows, deformable beds, finite element method, finite volume method, hybrid techniques, linear elasticity, shallow water equations

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Authors: Ku Yu Yiu

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Writers have included the ugly in their works for centuries, but ugliness has often served merely as a contrast to bring out the beautiful, not having emerged as an independent aesthetic category until recent history. In the 1980s, China was going through a series of changes and transformations; the wounds and scars from the Cultural Revolution, a freer literary atmosphere then, and the introduction of Western thoughts into China gave rise to a trend of penning the ugly and the repulsive among writers. Such trend of utilizing 'Ugliness' as a theme of writing in Chinese literature is especially observed among Xianfeng writers (China’s pioneer writers or avant-garde writers). As a prominent Xianfeng writer, Su Tong (1963-) also incorporates ugliness into his novels: shoddy environment, degenerate and ruthless society, distorted and decadent humanity are part and parcel of his deliberate efforts of exploring and depicting the ugly aspects of the world. His full-length novel Rice, staging the appalling protagonist Five Dragons, is a prime example. In fact, all characters in Rice exhibit Ugliness but Five Dragons’s turning into a figure of ugly spite is the most thorough and complete, making Rice a masterpiece of Su Tong’s art in projecting the Ugliness embedded in society and human nature. Approaching Rice from the angle of the aesthetics of the Ugly and selecting Five Dragons as the subject of close reading and analysis, this paper offers insights into both Su Tong’s distinct style of foregrounding and unfolding Ugliness in his novel and the workings of such text when he deploys the Ugly as a center component of his writing. In addition to citing from the discussion of Rice by literary critics and the author himself, this paper also presents textual evidence and analyzes the imageries/motifs and calculated vocabulary/narration employed by Su Tong to illustrate how Five Dragons' extreme behaviors and psychological states are integral to the plot and ultimately to the manifestation of ugliness as the novel’s theme. This study reveals that although the psyche and doings of Five Dragons and other 'ugly' characters are, as the author once stated, imagined products of the writer Su Tong himself, Rice sheds light onto the ugly aspects of life in China in 1920s-30s. Three aspects of Ugliness are identified and discussed in the paper. Lastly, this paper also suggests some effects of Su Tong’s exploration of Ugliness in Rice, proposing that the portrayal of Ugliness per se is not the ends of Su Tong’s mastery of the aesthetics of the Ugly but rather a means to making his writing transcend from provoking spontaneous moral judgment in readers on the doings of Five Dragons to prompting readers to ponder on philosophical questions such as how humanity can still be possible when an individual confronts the dark sides of a self, a society, and his/her fate.

Keywords: aesthetics, Rice, Su Tong, Ugly

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Authors: Seyyed Abbas Mojtabavi, Mojtaba Fatzaneh Moghadam, Masoud Mahdavi

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Steel Metal Shear Wall is one of the most common and widely used energy dissipation systems in structures, which is used today as a damping system due to the increase in the construction of metal structures. In the present study, the shear wall of the steel plate with dimensions of 5×3 m and thickness of 0.024 m was modeled with 2 floors of total height from the base level with finite element method in Abaqus software. The loading is done as a concentrated load at the upper point of the shear wall on the second floor based on step type buckle. The mesh in the model is applied in two directions of length and width of the shear wall, equal to 0.02 and 0.033, respectively, and the mesh in the models is of sweep type. Finally, it was found that the steel plate shear wall with cavity (CSPSW) compared to the SPSW model, S (Mises), Smax (In-Plane Principal), Smax (In-Plane Principal-ABS), Smax (Min Principal) increased by 53%, 70%, 68% and 43%, respectively. The presence of cavities has led to an increase in the estimated stresses, but their presence has caused critical stresses and critical deformations created to be removed from the inner surface of the shear wall and transferred to the desired sections (regular cavities) which can be suggested as a solution in seismic design and improvement of the structure to transfer possible damage during the earthquake and storm to the desired and pre-designed location in the structure.

Keywords: steel plate shear wall, abacus software, finite element method, , boundary element, seismic structural improvement, von misses stress

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Hassan Gholibeigian, Kazem Gholibeigian

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In this paper, our goal is determination of loading versus time in crust. For this goal, we present a computational procedure to propose a cumulative strain energy time profile which can be used to predict the approximate location and time of the next major earthquake (M > 4.5) along a specific fault, which we believe, is more accurate than many of the methods presently in use. In the coming pages, after a short review of the research works presently going on in the area of earthquake analysis and prediction, earthquake mechanisms in both the jerk and sequence earthquake direction is discussed, then our computational procedure is presented using differential equations of equilibrium which govern the nonlinear dynamic response of a system of finite elements, modified with an extra term to account for the jerk produced during the quake. We then employ Von Mises developed model for the stress strain relationship in our calculations, modified with the addition of an extra term to account for thermal effects. For calculation of the strain energy the idea of Pulsating Mantle Hypothesis (PMH) is used. This hypothesis, in brief, states that the mantle is under diurnal cyclic pulsating loads due to unbalanced gravitational attraction of the sun and the moon. A brief discussion is done on the Denali fault as a case study. The cumulative strain energy is then graphically represented versus time. At the end, based on some hypothetic earthquake data, the final results are verified.

Keywords: pulsating mantle hypothesis, inner core’s dislocation, outer core’s bulge, constitutive model, transient hydro-magneto-thermo-mechanical load, diurnal stress, jerk, fault behaviour

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Authors: S. Ziaran, M. Musil, M. Cekan, O. Chlebo

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The paper analyzes the response of buildings and industrially structures on seismic waves (low frequency mechanical vibration) generated by blasting operations. The principles of seismic analysis can be applied for different kinds of excitation such as: earthquakes, wind, explosions, random excitation from local transportation, periodic excitation from large rotating and/or machines with reciprocating motion, metal forming processes such as forging, shearing and stamping, chemical reactions, construction and earth moving work, and other strong deterministic and random energy sources caused by human activities. The article deals with the response of seismic, low frequency, mechanical vibrations generated by nearby blasting operations on a residential home. The goal was to determine the fundamental natural frequencies of the measured structure; therefore it is important to determine the resonant frequencies to design a suitable modal damping. The article also analyzes the package of seismic waves generated by blasting (Primary waves – P-waves and Secondary waves S-waves) and investigated the transfer regions. For the detection of seismic waves resulting from an explosion, the Fast Fourier Transform (FFT) and modal analysis, in the frequency domain, is used and the signal was acquired and analyzed also in the time domain. In the conclusions the measured results of seismic waves caused by blasting in a nearby quarry and its effect on a nearby structure (house) is analyzed. The response on the house, including the fundamental natural frequency and possible fatigue damage is also assessed.

Keywords: building structure, seismic waves, spectral analysis, structural response

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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Authors: H. Asadi, H. Naderan Tahan

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The aim of this paper is to investigate the effect of geometrical properties of helical fins in double pipe heat exchangers. On the other hand, the purpose of this project is to derive the hydraulic and thermal design tables and equations of double heat exchangers with helical fins. The numerical modeling is implemented to calculate the considered parameters. Design tables and correlated equations are generated by repeating the parametric numerical procedure for different fin geometries. Friction factor coefficient and Nusselt number are calculated for different amounts of Reynolds, fluid Prantle and fin twist angles for the range of laminar fluid flow in annular tube with helical fins. Results showed that friction factor coefficient and Nusselt number will be increased for higher Reynolds numbers and fins’ twist angles in general. These two parameters follow different patterns in response to Reynolds number increment. Thermal performance factor is defined to analyze these different patterns. Temperature and velocity contours are plotted against twist angle and number of fins to describe the changes in flow patterns in different geometries of twisted finned annulus. Finally twisted finned annulus friction factor coefficient, Nusselt Number and thermal performance factor are correlated by simulating the model in different design points.

Keywords: double pipe heat exchangers, heat exchanger performance, twisted fins, computational fluid dynamics

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Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

Abstract:

Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

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