Search results for: Philips mathematical model

Authors: Tarek M. Alguhane, Ayman H. Khalil, M. N. Fayed, Ayman M. Ismail

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A 15-storey RC building, studied in this paper, is representative of modern building type constructed in Madina City in Saudi Arabia before 10 years ago. These buildings are almost consisting of reinforced concrete skeleton, i. e. columns, beams and flat slab as well as shear walls in the stairs and elevator areas arranged in the way to have a resistance system for lateral loads (wind–earthquake loads). In this study, the dynamic properties of the 15-storey RC building were identified using ambient motions recorded at several spatially-distributed locations within each building. After updating the mathematical models for this building with the experimental results, three dimensional pushover analysis (nonlinear static analysis) was carried out using SAP2000 software incorporating inelastic material properties for concrete, infill and steel. The effect of modeling the building with and without infill walls on the performance point as well as capacity and demand spectra due to EQ design spectrum function in Madina area has been investigated. The response modification factor (R) for the 15 storey RC building is evaluated from capacity and demand spectra (ATC-40). The purpose of this analysis is to evaluate the expected performance of structural systems by estimating, strength and deformation demands in design, and comparing these demands to available capacities at the performance levels of interest. The results are summarized and discussed.

Keywords: seismic assessment, pushover analysis, ambient vibration, modal update

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Bouanati Sidi Mohammed, N. E. Chabane Sari, Mostefa Kara Selma

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It is well-known that Organic light emitting diodes (OLEDs) are attracting great interest in the display technology industry due to their many advantages, such as low price of manufacturing, large-area of electroluminescent display, various colors of emission included white light. Recently, there has been much progress in understanding the device physics of OLEDs and their basic operating principles. In OLEDs, Light emitting is the result of the recombination of electron and hole in light emitting layer, which are injected from cathode and anode. For improve luminescence efficiency, it is needed that hole and electron pairs exist affluently and equally and recombine swiftly in the emitting layer. The aim of this paper is to modeling polymer LED and OLED made with small molecules for studying the electrical and optical characteristics. The first simulation structures used in this paper is a mono layer device; typically consisting of the poly (2-methoxy-5(2’-ethyl) hexoxy-phenylenevinylene) (MEH-PPV) polymer sandwiched between an anode usually an indium tin oxide (ITO) substrate, and a cathode, such as Al. In the second structure we replace MEH-PPV by tris (8-hydroxyquinolinato) aluminum (Alq3). We choose MEH-PPV because of it's solubility in common organic solvents, in conjunction with a low operating voltage for light emission and relatively high conversion efficiency and Alq3 because it is one of the most important host materials used in OLEDs. In this simulation, the Poole-Frenkel- like mobility model and the Langevin bimolecular recombination model have been used as the transport and recombination mechanism. These models are enabled in ATLAS -SILVACO software. The influence of doping and thickness on I(V) characteristics and luminescence, are reported.

Keywords: organic light emitting diode, polymer lignt emitting diode, organic materials, hexoxy-phenylenevinylene

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Melkamu A. Zeru, Aweke A. Mitiku, Endashaw Amuka

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Background: Under-five mortality is the likelihood that a baby will pass away before turning exactly 5 years old, represented as a percentage per 1,000 live births. Exploring the spatial distribution and identifying the temporal pattern is important to reducing under-five child mortality globally, including in Ethiopia. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. Method: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data, which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality was used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. Results: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95 % CI (-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [𝛽 = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[𝛽= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[𝛽= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [𝛽= -0.6609, 95% CI (-0.8636, -0.4582)] and ANC visit > 4 times [𝛽= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. Conclusions: Even though the mortality rate for children under five has decreased over time, still there is still higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighbourhoods and temporal context when aiming to avoid under-five mortality.

Keywords: under-five children mortality, space-time dynamic, spatiotemporal, Ethiopia

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Authors: Liu Yi Hui

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The COVID-19 pandemic in 2020 has globally affected all aspects of life, with social distancing and quarantine orders causing turmoil and learning in community colleges being temporarily paused. In fact, this is the first time that adult education has faced such a severe challenge. It forces researchers to reflect on the impact of pandemics on adult education and ways to respond. Distance learning appears to be one of the pedagogical tools capable of dealing with interpersonal isolation and social distancing caused by the pandemic. This research aims to examine whether the impact of social distancing during COVID-19 will lead to increased acceptance of technology and, subsequently, an increase in adults ’ willingness to participate in distance learning. The hypothesis that social distancing and the desire to participate in distance learning affects learners’ tendency to accept technology is investigated. Teachers ’ participation in distance education and acceptance of technology are used as adjustment variables with the relationship to “social distancing,” “participation in distance learning,” and “acceptance of technology” of learners. A questionnaire survey was conducted over a period of twelve months for teachers and learners at all community colleges in Taiwan who enrolled in a basic unit course. Community colleges were separated using multi-stage cluster sampling, with their locations being metropolitan, non-urban, south, and east as criteria. Using the G*power software, 660 samples were selected and analyzed. The results show that through appropriate pedagogical strategies or teachers ’ own acceptance of technology, adult learners’ willingness to participate in distance learning could be influenced. A diverse model of participation can be developed, improving adult education institutions’ ability to plan curricula to be flexible to avoid the risk associated with epidemic diseases.

Keywords: social distancing, adult learning, community colleges, technology acceptance model

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Authors: Ayse Tarhan, Mustafa Ilkan, Mohammad Karimzadeh

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Science was considered as part of philosophy in ancient Greece. By the nineteenth century, it was understood that philosophy was very inclusive and that social and human sciences such as literature, history, and psychology should be separated and perceived as an autonomous branch of science. The computer was also first seen as a tool of mathematical science. Over time, computer science has grown by encompassing every area in which technology exists, and its growth compelled the division of computer science into different disciplines, just as philosophy had been divided into different branches of science. Now there is almost no branch of science in which computers are not used. One of the newer autonomous disciplines of computer science is digital humanities, and one of the areas of digital humanities is literature. The material of literature is words, and thanks to the software tools created using computer programming languages, data that a literature researcher would need months to complete, can be achieved quickly and objectively. In this article, three different tools that literary researchers can use in their work will be introduced. These studies were created with the computer programming languages Python and R and brought to the world of literature. The purpose of introducing the aforementioned studies is to set an example for the development of special tools or programs on Ottoman language and literature in the future and to support such initiatives. The first example to be introduced is the Stylometry tool developed with the R language. The other is The Metrical Tool, which is used to measure data in poems and was developed with Python. The latest literature analysis tool in this article is Voyant Tools, which is a multifunctional and easy-to-use tool.

Keywords: DH, literature, information technologies, stylometry, the metrical tool, voyant tools

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Authors: Meng Xu, Alexis K. H. Lau, Ming Xu, Bill Barron, Narges Shahraki

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As an emerging unconventional energy, shale gas has been an economically viable step towards a cleaner energy future in U.S. China also has shale resources that are estimated to be potentially the largest in the world. In addition, China has enormous unmet for a clean alternative to substitute coal. Nonetheless, the geological complexity of China’s shale basins and issues of water scarcity potentially impose serious constraints on shale gas development in China. Further, even if China could replicate to a significant degree the U.S. shale gas boom, China faces the problem of transporting the gas efficiently overland with its limited pipeline network throughput capacity and coverage. The aim of this study is to identify the potential bottlenecks in China’s gas transmission network, as well as to examine the shale gas development affecting particular supply locations and demand centers. We examine this through application of three scenarios with projecting domestic shale gas supply by 2020: optimistic, medium and conservative shale gas supply, taking references from the International Energy Agency’s (IEA’s) projections and China’s shale gas development plans. Separately we project the gas demand at provincial level, since shale gas will have more significant impact regionally than nationally. To quantitatively assess each shale gas development scenario, we formulated a gas pipeline optimization model. We used ArcGIS to generate the connectivity parameters and pipeline segment length. Other parameters are collected from provincial “twelfth-five year” plans and “China Oil and Gas Pipeline Atlas”. The multi-objective optimization model uses GAMs and Matlab. It aims to minimize the demands that are unable to be met, while simultaneously seeking to minimize total gas supply and transmission costs. The results indicate that, even if the primary objective is to meet the projected gas demand rather than cost minimization, there’s a shortfall of 9% in meeting total demand under the medium scenario. Comparing the results between the optimistic and medium supply of shale gas scenarios, almost half of the shale gas produced in Sichuan province and Chongqing won’t be able to be transmitted out by pipeline. On the demand side, the Henan province and Shanghai gas demand gap could be filled as much as 82% and 39% respectively, with increased shale gas supply. To conclude, the pipeline network in China is currently not sufficient in meeting the projected natural gas demand in 2020 under medium and optimistic scenarios, indicating the need for substantial pipeline capacity expansion for some of the existing network, and the importance of constructing new pipelines from particular supply to demand sites. If the pipeline constraint is overcame, Beijing, Shanghai, Jiangsu and Henan’s gas demand gap could potentially be filled, and China could thereby reduce almost 25% its dependency on LNG imports under the optimistic scenario.

Keywords: energy policy, energy systematic analysis, scenario analysis, shale gas in China

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Authors: Gichun Cha, Dongwan Lee, Junkyeong Kim, Aoqi Zhang, Seunghee Park

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All the world are studying the construction technology of double deck tunnel in order to respond to the increasing urban traffic demands and environmental changes. Advanced countries have the construction technology of the double deck tunnel structure. but the domestic country began research on it. Construction technologies are important. But Safety evaluation of structure is necessary to prevent possible accidents during construction. Thus, the double deck tunnel was required the shape management of middle slabs. The domestic country is preparing the construction of double deck tunnel for an alternate route and a pleasant urban environment. Shape management of double deck tunnel has been no research because it is a new attempted technology. The present, a similar study is bridge structure for the shape management. Bridge is implemented shape model using terrestrial laser scanning(TLS). Therefore, we proceed research on the bridge slabs because there is a similar structure of double deck tunnel. In the study, we develop shape management method of bridge slabs using TLS. We select the Test-bed for measurement site. This site is bridge located on Sungkyunkwan University Natural Sciences Campus. This bridge has a total length of 34m, the vertical height of 8.7m from the ground. It connects Engineering Building #1 and Engineering Building #2. Point cloud data for shape management is acquired the TLS and We utilized the Leica ScanStation C10/C5 model. We will confirm the Maximum displacement area of middle slabs using Least-Squares Fitting. We expect to raise stability for double deck tunnel through shape management for middle slabs.

Keywords: bridge slabs, least squares, safety evaluation, shape management method, terrestrial laser scanning

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Authors: Sangyeop Shin, D. C. M. Kulatunga, S. H. S. Dananjaya, Chamilani Nikapitiya, Jehee Lee, Mahanama De Zoysa

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Saprolegniasis is one of the most devastating fungal diseases in freshwater fish which is caused by species in the genus Saprolegnia including Saprolegnia parasitica. In this study, we isolated the strain of S. parasitica from diseased rainbow trout in Korea. Morphological and molecular based identification confirmed that isolated fungi belong to the member of S. parasitica, supported by its typical fungal features including cotton-like whitish mycelium, zoospores (primary and secondary) and phylogenetic analysis with internal transcribed spacer (ITS) region. Pathogenicity of isolated S. parasitica was developed in embryo, larvae, juvenile and adult zebrafish as a disease model. Up regulation of host genes encoding ZfTnf-α, Zfc-Rel, ZfIl-12, ZfLyz-c, Zfβ-def, and ZfHsp-70 was identified in zebrafish larvae after experimental challenge of S. parasitica showing the host immune responses against the S. parasitica. Survival of the juveniles upon fungal infection might be due to the increased immune protection in the host. Investigation of antifungal susceptibility of S. parasitica with natural lawsone (2-hydroxy-1,4-naphthoquinone) revealed the minimum inhibitory concentration (MIC) and percentage inhibition of radial growth (PIRG %) as 200 µg/mL and 31.8%, respectively. Lawsone was able to change the membrane permeability, and cause irreversible damage and disintegration to the cellular membranes of S. parasitica which might have effect on fungi growth inhibition. Moreover, the mycelium exposed to lawsone (MIC level) changed the transcriptional responses of S. parasitica genes. Overall results indicate that lawsone could be a potential and novel anti-S. parasitica agent for controlling S. parasitica infection.

Keywords: host-pathogen interactions, lawsone, rainbow trout, Saprolegnia parasitica, Saprolegniasis, zebrafish

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Authors: Kechnit Djamel, Ammarri Abdelhadi, Raphael Tshimang, Mark Trrig

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One of the most important aspects of monitoring rivers is navigation. The variation of discharge in the river generally produces a change in available draft for a vessel, particularly in the low flow season, which can impact the navigable water path, especially when the water depth is less than the normal one, which allows safe navigation for boats. The water depth is related to the bathymetry of the channel as well as the discharge. For a seasonal update of the navigation maps, a daily discharge value is required. Many novel approaches based on earth observation and remote sensing have been investigated for large rivers. However, it should be noted that most of these approaches are not currently able to directly estimate river discharge. This paper discusses the application of remote sensing tools using the analysis of the reflectance value of MODIS imagery and is combined with field measurements for the estimation of discharge. This approach is applied in the lower reach of the Congo River (Pool Malebo) for the period between 2019 and 2021. The correlation obtained between the observed discharge observed in the gauging station and the reflectance ratio time series is 0.81. In this context, a Discharge Reflectance Model (DRM) was developed to express discharge as a function of reflectance. This model introduces a non-contact method that allows discharge monitoring using earth observation. DRM was validated by field measurements using ADCP, in different sections on the Pool Malebo, over two different periods (dry and wet seasons), as well as by the observed discharge in the gauging station. The observed error between the estimated and measured discharge values ranges from 1 to 8% for the ADCP and from (1% to 11%) for the gauging station. The study of the uncertainties will give us the possibility to judge the robustness of the DRM.

Keywords: discharge monitoring, navigation, MODIS, empiric, ADCP, Congo River

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Authors: Rukayya Ibrahim Muazu, Aiduan Li Borrion, Julia A. Stegemann

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Biomass briquette gasification is regarded as a promising route for efficient briquette use in energy generation, fuels and other useful chemicals, however, previous research work has focused on briquette gasification in fixed bed gasifiers such as updraft and downdraft gasifiers. Fluidised bed gasifier has the potential to be effectively sized for medium or large scale. This study investigated the use of fuel briquettes produced from blends of rice husks and corn cobs biomass residues, in a bubbling fluidised bed gasifier. The study adopted a combination of numerical equations and Aspen Plus simulation software to predict the product gas (syngas) composition based on briquette's density and biomass composition (blend ratio of rice husks to corn cobs). The Aspen Plus model was based on an experimentally validated model from the literature. The results based on a briquette size of 32 mm diameter and relaxed density range of 500 to 650 kg/m3 indicated that fluidisation air required in the gasifier increased with an increase in briquette density, and the fluidisation air showed to be the controlling factor compared with the actual air required for gasification of the biomass briquettes. The mass flowrate of CO2 in the predicted syngas composition, increased with an increase in the air flow rate, while CO production decreased and H2 was almost constant. The H2/CO ratio for various blends of rice husks and corn cobs did not significantly change at the designed process air, but a significant difference of 1.0 for H2/CO ratio was observed at higher air flow rate, and between 10/90 to 90/10 blend ratio of rice husks to corn cobs. This implies the need for further understanding of biomass variability and hydrodynamic parameters on syngas composition in biomass briquette gasification.

Keywords: aspen plus, briquettes, fluidised bed, gasification, syngas

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: E. Tandis, E. Assareh

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Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

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Authors: Esra Turker, Umit Hakan Yildiz, Ahu Arslan Yildiz

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The key of regenerative medicine is mimicking natural three dimensional (3D) microenvironment of tissues by utilizing appropriate biomaterials. In this study, a synthetic biodegradable polymer; poly (L-lactide-co-ε-caprolactone) (PLLCL) and a natural polymer; collagen was used to mimic the biochemical structure of the natural extracellular matrix (ECM), and by means of electrospinning technique the real physical structure of ECM has mimicked. PLLCL/Collagen biocomposite scaffolds enables cell attachment, proliferation and nutrient transport through fabrication of micro to nanometer scale nanofibers. Biocomposite materials are commonly preferred due to limitations of physical and biocompatible properties of natural and synthetic materials. Combination of both materials improves the strength, degradation and biocompatibility of scaffold. Literature studies have shown that collagen is mostly solved with heavy chemicals, which is not suitable for cell culturing. To overcome this problem, a new approach has been developed in this study where polyvinylpyrrolidone (PVP) is used as co-electrospinning agent. PVP is preferred due to its water solubility, so PLLCL/collagen biocomposite scaffold can be easily and rapidly produced. Hydrolytic and enzymatic biodegradation as well as mechanical strength of scaffolds were examined in vitro. Cell adhesion, proliferation and cell morphology characterization studies have been performed as well. Further, on-chip drug screening analysis has been performed over 3D tumor models. Overall, the developed biocomposite scaffold was used for 3D tumor model formation and obtained results confirmed that developed model could be used for drug screening studies to predict clinical efficacy of a drug.

Keywords: biomaterials, 3D cell culture, drug screening, electrospinning, lab-on-a-chip, tissue engineering

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Authors: Shuo Wang

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The hydrogeological parameters of the engineering site and the hydraulic connection between the aquifers can be obtained by the pumping test. Through the recovery test, the characteristics of water level recovery and the law of surface subsidence recovery can be understood. The above two tests can provide the basis for subsequent engineering design. At present, the deformation of deep soil caused by pumping tests is often neglected. However, some studies have shown that the maximum settlement subject to groundwater drawdown is not necessarily on the surface but in the deep soil. In addition, the law of settlement recovery of each soil layer subject to water level recovery is not clear. If the deformation-sensitive structure is deep in the test site, safety accidents may occur. In this study, the pumping test and recovery test of a confined aquifer in Shanghai are introduced. The law of measured groundwater changes and surface subsidence are analyzed. In addition, the fluid-solid coupling model was established by ABAQUS based on the Biot consolidation theory. The models are verified by comparing the computed and measured results. Further, the variation law of water level and the deformation law of deep soil during pumping and recovery tests under different site conditions and different times and spaces are discussed through the above model. It is found that the maximum soil settlement caused by pumping in a confined aquifer is related to the permeability of the overlying aquitard and pumping time. There is a lag between soil deformation and groundwater changes, and the recovery rate of settlement deformation of each soil layer caused by the rise of water level is different. Finally, some possible research directions are proposed to provide new ideas for academic research in this field.

Keywords: coupled hydro-mechanical analysis, deep ground settlement, numerical simulation, pumping test, recovery test

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Authors: Peri Ozlem Yuksel-Sokmen, Irfan Cagtay

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Oral languages are destined to disappear rapidly in the absence of interventions aimed at encouraging their usage by young children. The seminal language preservation model proposed by Fishman (1991) stresses the importance of multiple generations using the endangered L1 while engaged in daily routines with younger children. Over the last two decades Fishman (2001) has used his intergenerational transmission model in documenting the revitalization of Basque languages, providing evidence that families are transmitting Euskara as a first language to their children with success. In our study, to motivate usage of Lazuri, we asked caregivers to speak the language while engaged with their toddlers (12 to 48 months) in semi-structured play, and included both parents (N=32) and grandparents (N=30) as play partners. This unnatural prompting to speak only in Lazuri was greeted with reluctance, as 90% of our families indicated that they had stopped using Lazuri with their children. Nevertheless, caregivers followed instructions and produced 67% of their utterances in Lazuri, with another 14% of utterances using a combination of Lazuri and Turkish (Codeswitch). Although children spoke mostly in Turkish (83% of utterances), frequencies of caregiver utterances in Lazuri or Codeswitch predicted the extent to which their children used the minority language in return. This trend suggests that home interventions aimed at encouraging dyads to communicate in a non-preferred, endangered language can effectively increase children’s usage of the language. Alternatively, this result suggests than any use of the minority language on the part of the children will promote its further usage by caregivers. For researchers examining links between play, culture, and child development, structured play has emerged as a critical methodology (e.g., Frost, Wortham, Reifel, 2007, Lilliard et al., 2012; Sutton-Smith, 1986; Gaskins & Miller, 2009), allowing investigation of cultural and individual variation in parenting styles, as well as the role of culture in constraining the affordances of toys. Toy props, as well as picture-books in native languages, can be used as tools in the transmission and preservation of endangered languages by allowing children to explore adult roles through enactment of social routines and conversational patterns modeled by caregivers. Through adult-guided play children not only acquire scripts for culturally significant activities, but also develop skills in expressing themselves in culturally relevant ways that may continue to develop over their lives through community engagement. Further pedagogical tools, such as language games and e-learning, will be discussed in this proposed oral talk.

Keywords: language intervention, pedagogical tools, endangered languages, Lazuri

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Authors: Dimitra V. Peristeri, Hussameldin M. Nour, Amiya Ahsan, Sameh Abogabal, Krishna K. Singh, Muhammad Shafique Sajid

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Introduction: The use of optimal skin antiseptic agents for the prevention of surgical site infection (SSI) is of critical importance, especially during abdominal surgical procedures. Alcohol-based chlorhexidine gluconate (CHG) and aqueous-based povidone-iodine (PVI) are the two most common skin antiseptics used nowadays. The objective of this article is to evaluate the effectiveness of alcohol-based CHG versus aqueous-based PVI used for skin preparation before abdominal surgery to reduce SSIs. Methods: Standard medical databases such as MEDLINE, Embase, Pubmed, and Cochrane Library were searched to find randomised, controlled trials (RCTs) comparing alcohol-based CHG skin preparation versus aqueous-based PVI in patients undergoing abdominal surgery. The combined outcomes of SSIs were calculated using an odds ratio (OR) with 95% confidence intervals (95% CI). All data were analysed using Review Manager (RevMan) Software 5.4, and the meta-analysis was performed with a random effect model analysis. Results: A total of 11 studies, all RCTs, were included (n= 12072 participants), recruiting adult patients undergoing abdominal surgery. In the random effect model analysis, the use of alcohol-based CHG in patients undergoing abdominal surgery was associated with a reduced risk of SSI compared to aqueous-based PVI (OR: 0.84; 95% CI [0.74, 0.96], z= 2.61, p= 0.009). Conclusion: Alcohol-based CHG may be more effective for preventing the risk of SSI compared to aqueous-based PVI agents in abdominal surgery. The conclusion of this meta-analysis may add a guiding value to reinforce current clinical practice guidelines.

Keywords: skin preparation, surgical site infection, chlorhexidine, skin antiseptics

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Authors: Nur Atiqah Adam, Mor Jack Ng, Bernard Chern, Kok Hian Tan

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Introduction: Gestational diabetes (GDM) is a common pregnancy complication with short and long-term health consequences for both mother and fetus. Factors such as family history of diabetes mellitus, maternal obesity, maternal age, ethnicity and parity have been reported to influence the risk of GDM. In a multi-racial country like Singapore, it is worthwhile to study the GDM prevalences of different ethnicities. We aim to investigate the influence of ethnicity on the racial prevalences of GDM in Singapore. This is important as it may help us to improve guidelines on GDM healthcare services according to significant risk factors unique to Singapore. Materials and Methods: Obstetric cohort data of 926 singleton deliveries in KK Women’s and Children’s Hospital (KKH) from 2011 to 2013 was obtained. Only patients aged 18 and above and without complicated pregnancies or chronic illnesses were targeted. Factors such as ethnicity, maternal age, parity and maternal body mass index (BMI) at booking visit were studied. A multivariable logistic regression model, adjusted for confounders, was used to determine which of these factors are significantly associated with an increased risk of GDM. Results: The overall GDM prevalence rate based on WHO 1999 criteria & at risk screening (race alone not a risk factor) was 8.86%. GDM rates were higher among women above 35 years old (15.96%), obese (15.15%) and multiparous women (10.12%). Indians had a higher GDM rate (13.0 %) compared to the Chinese (9.57%) and Malays (5.20%). However, using multiple logistic regression model, variables that are significantly related to GDM rates were maternal age (p < 0.001) and maternal BMI at booking visit (p = 0.006). Conclusion: Maternal age (p < 0.001) and maternal booking BMI (p = 0.006) are the strongest risk factors for GDM. Ethnicity per se does not seem to have a significant influence on the prevalence of GDM in Singapore (p = 0.064). Hence we should tailor guidelines on GDM healthcare services according to maternal age and booking BMI rather than ethnicity.

Keywords: ethnicity, gestational diabetes, healthcare, pregnancy

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Authors: Lie-Fen Lin, Tzu-Hsuan Lin, Ching-Heng Lin

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Hospice care for terminally ill patients provides not only a better quality of life but also cost-saving benefits. However, the utilization of home-based hospice care (HBH care) remains low even for countries covered by National Health Insurance (NHI) programs in Taiwan. In the current commercial insurance policy, only hospital-based hospice benefits were covered. It may have an influence on the insureds chosen to receive end-of-life care in a hospitalized manner. Thus, how to propose a feasible method to advocate HBH care utilization rate of public health policies is an important issue. A total of 130,219 cancer decedents in the year 2011-2013 from the National Health Insurance Research Database (NHIRD) in Taiwan were included in this study. By adding a day volume pays benefits of HBH care as a commercial insurance rider, will provide alternative benefits for the insureds. A multiple-state Markov chain model was incorporated to estimate the transition intensities of patients in different states at the end of their lives (Non-hospice, HBH, hospital-based hospice), and the premiums were estimated. HBH care insurance benefits provide financial support and reduce the burden of care for patients. The rate-making of this product is very sensitive while the utilization rate is rising, especially for high ages. The proposed HBH care insurance is a feasible way to reduce the financial burden, enhance the care quality and family satisfaction of insureds. Meanwhile, insurance companies can participate in advocating a good medical policy to enhance the social image. In addition, the medical costs of NHI can reduce effectively.

Keywords: home-based hospice care, commercial insurance, Markov chain model, the day volume pays

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Authors: Ashleigh Williams

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Micro- and nanoplastics’ (MNLPs) omnipresence and ecological accumulation is evident when surveying recent environmental impact studies. For example, in 2014 it was estimated that at least 52.3 trillion plastic microparticles are floating at sea, and scientists have even found plastics present remote Arctic ice and snow (5,6). Plastics have even found their way into precipitation, with more than 1000 tons of microplastic rain precipitating onto the Western United States in 2020. Even more recent studies evaluating the chemical safety of reusable plastic bottles found that hundreds of chemicals leached into the control liquid in the bottle (ddH2O, ph = 7) during a 24-hour time period. A consequence of the increased abundance in plastic waste in the air, land, and water every year is the bioaccumulation of MNLPs in ecosystems and trophic niches of the animal food chain, which could potentially cause increased direct and indirect exposure of humans to MNLPs via inhalation, ingestion, and dermal contact. Though the detrimental, toxic effects of MNLPs have been established in marine biota, much less is known about the potentially hazardous health effects of chronic MNLP ingestion in humans. Recent data indicate that long-term exposure to MNLPs could cause possible inflammatory and dysbiotic effects. However, toxicity seems to be largely dose-, as well as size-dependent. In addition, the transcytotic uptake of MNLPs through the intestinal epithelia in humans remain relatively unknown. To this point, the goal of the current study was to investigate the mechanisms of micro- and nanoplastic uptake and transcytosis of Polystyrene (PE) in human stem-cell derived, physiologically relevant in vitro intestinal model systems, and to compare the relative effect of particle size (30 nm, 100 nm, 500 nm and 1 µm), and concentration (0 µg/mL, 250 µg/mL, 500 µg/mL, 1000 µg/mL) on polystyrene MNLP uptake, transcytosis and intestinal epithelial model integrity. Observational and quantitative data obtained from confocal microscopy, immunostaining, transepithelial electrical resistance (TEER) measurements, cryosectioning, and ELISA cytokine assays of the proinflammatory cytokines Interleukin-6 and Interleukin-8 were used to evaluate the localization and transcytosis of polystyrene MNPs and its impact on epithelial integrity in human-derived intestinal in vitro model systems. The effect of Microfold (M) cell induction on polystyrene micro- and nanoparticle (MNP) uptake, transcytosis, and potential inflammation was also assessed and compared to samples grown under standard conditions. Microfold (M) cells, link the human intestinal system to the immune system and are the primary cells in the epithelium responsible for sampling and transporting foreign matter of interest from the lumen of the gut to underlying immune cells. Given the uptake capabilities of Microfold cells to interact both specifically and nonspecific to abiotic and biotic materials, it was expected that M- cell induced in vitro samples would have increased binding, localization, and potentially transcytosis of Polystyrene MNLPs across the epithelial barrier. Experimental results of this study would not only help in the evaluation of the plastic toxicity, but would allow for more detailed modeling of gut inflammation and the intestinal immune system.

Keywords: nanoplastics, enteroids, intestinal barrier, tissue engineering, microfold (M) cells

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Authors: Pablo Martin, Jorge Olivares, Fernando Maass

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A method to obtain analytic approximations for special function of interest in engineering and physics is described here. Each approximate function will be valid for every positive value of the variable and accuracy will be high and increasing with the number of parameters to determine. The general technique will be shown through an application to the modified Bessel function of order zero, I₀(x). The form and the calculation of the parameters are performed with the simultaneous use of the power series and asymptotic expansion. As in Padé method rational functions are used, but now they are combined with other elementary functions as; fractional powers, hyperbolic, trigonometric and exponential functions, and others. The elementary function is determined, considering that the approximate function should be a bridge between the power series and the asymptotic expansion. In the case of the I₀(x) function two analytic approximations have been already determined. The simplest one is (1+x²/4)⁻¹/⁴(1+0.24273x²) cosh(x)/(1+0.43023x²). The parameters of I₀(x) were determined using the leading term of the asymptotic expansion and two coefficients of the power series, and the maximum relative error is 0.05. In a second case, two terms of the asymptotic expansion were used and 4 of the power series and the maximum relative error is 0.001 at x≈9.5. Approximations with much higher accuracy will be also shown. In conclusion a new technique is described to obtain analytic approximations to some functions of interest in sciences, such that they have a high accuracy, they are valid for every positive value of the variable, they can be integrated and differentiated as the usual, functions, and furthermore they can be calculated easily even with a regular pocket calculator.

Keywords: analytic approximations, mathematical-physics applications, quasi-rational functions, special functions

Procedia PDF Downloads 240

Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Yue Huang, Hongcheng Gan

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The new urbanization pattern has led to a rapid growth in demand for short-distance intercity travel, and the emergence of new travel modes has also increased the variety of intercity travel options. In previous studies on intercity travel mode choice behavior, the impact of functional amenities of travel mode and travelers’ long-term personality characteristics has rarely been considered, and empirical results have typically been calibrated using revealed preference (RP) or stated preference (SP) data. This study designed a questionnaire that combines the RP and SP experiment from the perspective of a trip chain combining inner-city and intercity mobility, with consideration for the actual condition of the Huainan-Hefei traffic corridor. On the basis of RP/SP fusion data, a hybrid choice model considering both random taste heterogeneity and psychological characteristics was established to investigate travelers’ mode choice behavior for traditional train, high-speed rail, intercity bus, private car, and intercity online car-hailing. The findings show that intercity time and cost exert the greatest influence on mode choice, with significant heterogeneity across the population. Although inner-city cost does not demonstrate a significant influence, inner-city time plays an important role. Service attributes of travel mode, such as catering and hygiene services, as well as free wireless network supply, only play a minor role in mode selection. Finally, our study demonstrates that safety-seeking tendency, hedonism, and introversion all have differential and significant effects on intercity travel mode choice.

Keywords: intercity travel mode choice, stated preference survey, hybrid choice model, RP/SP fusion data, psychological latent variable, heterogeneity

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Authors: Innocent Uzougbo Onwuegbuzie

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Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.

Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan

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Authors: Mahesh K. Jat, Ankan Jana, Mahender Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) are used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: LULC, sensible heat flux, latent heat flux, SEBAL, landsat, precipitation, temperature

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Authors: J. Raub, H. Schillok, L. Kaupe, C. Jung-Sievers, G. Pitschel-Walz, M. Bühner, J. Gensichen, F. D. Pokal-Gruppe

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Introduction: In Germany, depression ranks among the top ten diseases with the highest disease burden and often occurs with comorbidities. Collaborative Care (CC), a concept developed in the United States for the primary care management of chronic diseases, has been identified as an efficient model for the treatment of depression in general medicine. A recent meta-analysis highlights research gaps regarding CC in patients with psychiatric multimorbidity. The highest prevalence of psychiatric comorbidities in depression is observed in anxiety disorders, post-traumatic stress disorder (PTSD), and substance use disorders. Methods: We conducted a literature search following the PRISMA guidelines with three components: Collaborative Care, Depression and randomized controlled trial on the common databases. We focused on the examination of psychiatric comorbidities in depression, specifically Posttraumatic Stress Disorder (PTSD) and Substance Use Disorder (SUD). Results: During the screening process, we identified nine relevant articles related to PTSD, the number of articles related to Substance Use Disorder (SUD) was ten. We examined a total of 8,634 individuals. Our literature review did not reveal any overall significant superiority of the Collaborative Care model compared to Usual Care in patients with depression with comorbid Substance Use Disorder (SUD) or Posttraumatic Stress Disorder (PTSD). Discussion: Five studies demonstrate a faster and statistically significant improvement in depression outcomes among patients with Substance Use Disorder (SUD) and Posttraumatic Stress Disorder (PTSD). Currently, several randomized controlled trials on the topic of Collaborative Care in depression with psychiatric comorbidity are ongoing, such as miCare, Claro and COMET.

Keywords: Depression, primary care, collaborative care, PTSD, Substance use Disorder

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Archibong Eso

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Gas turbine operators have been faced with the dramatic financial setback resulting from compressor fouling. In a highly deregulated power industry where there is stiffness in the market competition, has made it imperative to improvise means of reducing maintenance cost in other to yield maximum profit. Compressor fouling results from the deposition of contaminants in the presence of oil and moisture on the compressor blade or annulus surfaces, which leads to a loss in flow capacity and compressor efficiency. These combined effects reduce power output, increase heat rate and cause creep life reduction. This paper also contains a model of two gas turbine engines via Cranfield University software known as TURBOMATCH, which is simulation software for detecting engine fouling rate. The model engines are of different configurations and capacities, and are operating in two different modes of constant output power and turbine inlet temperature for a two and three stage filter system. The idea is to investigate the more economically viable filtration systems by gas turbine users based on performance only. It has been demonstrated in the results that the two spool engine is a little more beneficial compared to the single spool. This is as a result of a higher pressure ratio of the two spools as well as the deceleration of the high-pressure compressor and high-pressure turbine speed in a constant TET. Meanwhile, the inlet filtration system was properly designed and balanced with a well-timed and economical compressor washing regime/scheme to control compressor fouling. The different technologies of inlet air filtration and compressor washing are considered and an attempt at optimization with respect to the cost of a combination of both control measures are made.

Keywords: inlet filtration, pressure loss, single spool, two spool

Procedia PDF Downloads 306

Authors: M. Mulumpwa, W. W. L. Jere, M. Lazaro, A. H. N. Mtethiwa

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The study considered Seasonal Autoregressive Integrated Moving Average (SARIMA) processes to select an appropriate stochastic model to forecast the monthly data from the Lake Malawi water levels for the period 1986 through 2015. The appropriate model was chosen based on SARIMA (p, d, q) (P, D, Q)S. The Autocorrelation function (ACF), Partial autocorrelation (PACF), Akaike Information Criteria (AIC), Bayesian Information Criterion (BIC), Box–Ljung statistics, correlogram and distribution of residual errors were estimated. The SARIMA (1, 1, 0) (1, 1, 1)12 was selected to forecast the monthly data of the Lake Malawi water levels from August, 2015 to December, 2021. The plotted time series showed that the Lake Malawi water levels are decreasing since 2010 to date but not as much as was the case in 1995 through 1997. The future forecast of the Lake Malawi water levels until 2021 showed a mean of 474.47 m ranging from 473.93 to 475.02 meters with a confidence interval of 80% and 90% against registered mean of 473.398 m in 1997 and 475.475 m in 1989 which was the lowest and highest water levels in the lake respectively since 1986. The forecast also showed that the water levels of Lake Malawi will drop by 0.57 meters as compared to the mean water levels recorded in the previous years. These results suggest that the Lake Malawi water level may not likely go lower than that recorded in 1997. Therefore, utilisation and management of water-related activities and programs among others on the lake should provide room for such scenarios. The findings suggest a need to manage the Lake Malawi jointly and prudently with other stakeholders starting from the catchment area. This will reduce impacts of anthropogenic activities on the lake’s water quality, water level, aquatic and adjacent terrestrial ecosystems thereby ensuring its resilience to climate change impacts.

Keywords: forecasting, Lake Malawi, water levels, water level fluctuation, climate change, anthropogenic activities

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

Procedia PDF Downloads 73