Search results for: multiple stream model

Authors: Patricia Kopetz

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Too often our youngest children with disabilities receive diagnostic labels and accompanying treatment plans based upon perceptions that the children are of limited aptitude and/or ambition. However, children of varied-ability levels who are diagnosed with ‘multiple disabilities,’ can participate and excel in school-based instruction that aligns with their desires, interests, and fortitude – criteria components not foretold by scores on standardized assessments. The paper represents theoretical work in Special Education Innovative Instruction, and includes presenting research materials, some developed by the author herself. The majority of students with disabilities are now served in general education settings in the United States, embracing inclusive practices in our schools. ‘There is now a stronger call for special education to step up and improve efficiency, implement evidence-based practices, and provide greater accountability on key performance indicators that support successful academic and post-school outcomes for students with disabilities.’ For example, in the United States, the Office of Special Education Programs (OSEP) is focusing on results-driven indicators to improve outcomes for students with disabilities. School personnel are appreciating the implications of research-driven approaches for students diagnosed with multiple disabilities, and aim to align their practices toward such focus. The paper presented will provide updates on current theoretical principles and perspectives, and explore advancements in latest, evidence-based and results-driven instructional practices that can motivate children with multiple disabilities to advance their skills and engage in learning activities that as nonconventional, innovative, and proven successful.

Keywords: childhood special education, educational technology , innovative instruction, multiple disabilities

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Seyedtaghi Mirmohammadi, Jamshid Yazdani, Syavash Etemadi Nejad

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One of the main indoor air parameters in classrooms is dust pollution and it depends on the particle size and exposure duration. However, there is a lake of data about the exposure level to PM2.5 concentrations in rural area classrooms. The objective of the current study was exposure assessment for PM2.5 for students in the classrooms. One year monitoring was carried out for fifteen schools by time-series sampling to evaluate the indoor air PM2.5 in the rural district of Sari city, Iran. A hygrometer and thermometer were used to measure some psychrometric parameters (temperature, relative humidity, and wind speed) and Real-Time Dust Monitor, (MicroDust Pro, Casella, UK) was used to monitor particulate matters (PM2.5) concentration. The results show the mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3. The regression model indicated that a positive correlation between indoor PM2.5 concentration and relative humidity, also with distance from city center and classroom size. Meanwhile, the regression model revealed that the indoor PM2.5 concentration, the relative humidity, and dry bulb temperature was significant at 0.05, 0.035, and 0.05 levels, respectively. A statistical predictive model was obtained from multiple regressions modeling for indoor PM2.5 concentration and indoor psychrometric parameters conditions.

Keywords: classrooms, concentration, humidity, particulate matters, regression

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Authors: Ziyan Zhang

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Although the Synthetic Control Method (SCM) is now widely applied, its most commonly- used inference method, placebo test, is often problematic, especially when the treatment is not uniquely assigned. This paper discusses the problems with the placebo test under the multivariate treatment case. And, to improve the power of inferences, I further propose an Andrews-type procedure as it potentially solves some drawbacks of the placebo test. Simulations are conducted to show the Andrews’ test is often valid and powerful, compared with the placebo test.

Keywords: Synthetic Control Method, Multiple treatments, Andrews' test, placebo test

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Authors: Ogbonnaya Elom, Francis N. Azunku, Ogochukwu Onah

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This study was carried out to determine the correlates of cost effectiveness analysis of rating scale and psycho-productive multiple choice test for assessing students’ performance in rice production. Four research questions were developed and answered, while one hypothesis was formulated and tested. Survey and correlation designs were adopted. The population of the study was 20,783 made up of 20,511 senior secondary (SSII) students and 272 teachers of agricultural science from 221 public secondary schools. Two schools with one intact class of 30 students each was purposely selected as sample based on certain criteria. Four sets of instruments were used for data collection. One of the instruments-the rating scale, was subjected to face and content validation while the other three were subjected to face validation only. Cronbach alpha technique was utilized to determine the internal consistency of the rating scale items which yielded a coefficient of 0.82 while the Kudder-Richardson (K-R 20) formula was involved in determining the stability of the psycho-productive multiple choice test items which yielded a coefficient of 0.80. Method of data collection involved a step-by-step approach in collecting data. Data collected were analyzed using percentage, weighted mean and sign test to answer the research questions while the hypothesis was tested using Spearman rank-order of correlation and t-test statistic. Findings of the study revealed among others, that psycho-productive multiple choice test is more effective than rating scale when the former is applied on the two groups of students. It was recommended among others, that the external examination bodies should integrate the use of psycho- productive multiple choice test into their examination policy and direct secondary schools to comply with it.

Keywords: correlates, cost-effectiveness, psycho-productive multiple-choice scale, rating scale

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Authors: Tamer Mesallam, Medhat Yousef, Ayna Almasaad

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BACKGROUND: Cochlear implantation (CI) in children with additional disabilities can be a fundamental and supportive intervention. Although, there may be some positive impacts of CI on children with multiple disabilities such as better outcomes of communication skills, development, and quality of life, the families of those children complain from the post-implant habilitation efforts that considered as a burden. OBJECTIVE: To investigate the outcomes of CI children with different co-disabilities through using the Meaningful Auditory Integration Scale (MAIS) and the Meaningful Use of Speech Scale (MUSS) as outcome measurement tools. METHODS: The study sample comprised 25 hearing-impaired children with co-disability who received cochlear implantation. Age and gender-matched control group of 25 cochlear-implanted children without any other disability has been also included. The participants' auditory skills and speech outcomes were assessed using MAIS and MUSS tests. RESULTS: There was a statistically significant difference in the different outcomes measure between the two groups. However, the outcomes of some multiple disabilities subgroups were comparable to the control group. Around 40% of the participants with co-disabilities experienced advancement in their methods of communication from behavior to oral mode. CONCLUSION: Cochlear-implanted children with multiple disabilities showed variable degrees of auditory and speech outcomes. The degree of benefits depends on the type of the co-disability. Long-term follow-up is recommended for those children.

Keywords: children with disabilities, Cochlear implants, hearing impairment, language development

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Authors: Anuraj Singh

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The complex dynamics is explored in a prey predator system with multiple delays. The predator dynamics is governed by Leslie-Gower scheme. The existence of periodic solutions via Hopf bifurcation with respect to delay parameters is established. To substantiate analytical findings, numerical simulations are performed. The system shows rich dynamic behavior including chaos and limit cycles.

Keywords: chaos, Hopf bifurcation, stability, time delay

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Authors: WooJin KANG

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Contrary to the ideal, in the cartelized democracy, meritocracy is revealed to be a system that gives arrogance to the winners and humiliation to the losers, and more and more studies are asserting the upper-class bias of meritocracy. However, only some studies have analyzed the determinants of the perception of meritocracy and fairness among young people. This article was an attempt to fill this gap. According to the empirical results of this article, the determinants of fairness of the meritocracy in the youth were multidimensional. The social status model, the political ideology model, and the future prospect model all significantly impacted the perception of meritocracy fairness among young people. Contrary to the predictions of the system justification theory and the compensatory control theory of previous studies, the lower-class youth were critical of meritocracy. In addition, the more negative the future outlook, the less they accepted the fairness of meritocracy. In addition, ideological debates over solutions to inequality of opportunity, which began in earnest during the 20th presidential election, turned out to be a variable that significantly influenced the perception of fairness based on meritocracy among young people. The results of the empirical analysis in this article reaffirmed the multidimensional structure of the youth. This suggests the need for policy responses leading to education tailored to various subgroups within the youth.

Keywords: Meritocracy, Exam-Meritocracy, Fairness, Multiple-inequality

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: Yang Luo, Chang Nyung Kim

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This study numerically analyzes a steady-state, three-dimensional liquid-metal magnetohydrodynamic flows in a conduit with multiple channels under a uniform magnetic field. The geometry of the conduit is of a four-parallel-channels system including one inflow channel and three outflow channels. The liquid-metal flows in the inflow channel, then turns 1800 in the transition segment, finally flows into three different outflow channels simultaneously. This kind of channel system can induce counter flow and co-flow, which is rarely investigated before. The axial velocity in the side layer near the first partitioning wall, which is located between the inflow channel and the first outflow channel, is the highest. ‘M-shaped’ velocity profiles are obtained in the side layers of the inflow and outflow channels. The interdependency of the current, fluid velocity, pressure, electric potential is examined in order to describe the electromagnetic characteristics of the liquid-metal flows.

Keywords: liquid-metal, multiple channels, magnetic field, magnetohydrodynamic

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Authors: S. Lertsakulpasuk, S. Athichanagorn

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As depletion-drive gas reservoirs are abandoned when there is insufficient production rate due to pressure depletion, waterflooding has been proposed to increase the reservoir pressure in order to prolong gas production. Due to high cost, water injection may not be economically feasible. Water dumpflood into gas reservoirs is a new promising approach to increase gas recovery by maintaining reservoir pressure with much cheaper costs than conventional waterflooding. Thus, a simulation study of water dumpflood into multiple nearly abandoned or already abandoned thin-bedded gas reservoirs commonly found in the Gulf of Thailand was conducted to demonstrate the advantage of the proposed method and to determine the most suitable operational parameters for reservoirs having different system parameters. A reservoir simulation model consisting of several thin-layered depletion-drive gas reservoirs and an overlying aquifer was constructed in order to investigate the performance of the proposed method. Two producers were initially used to produce gas from the reservoirs. One of them was later converted to a dumpflood well after gas production rate started to decline due to continuous reduction in reservoir pressure. The dumpflood well was used to flow water from the aquifer to increase pressure of the gas reservoir in order to drive gas towards producer. Two main operational parameters which are wellhead pressure of producer and the time to start water dumpflood were investigated to optimize gas recovery for various systems having different gas reservoir dip angles, well spacings, aquifer sizes, and aquifer depths. This simulation study found that water dumpflood can increase gas recovery up to 12% of OGIP depending on operational conditions and system parameters. For the systems having a large aquifer and large distance between wells, it is best to start water dumpflood when the gas rate is still high since the long distance between the gas producer and dumpflood well helps delay water breakthrough at producer. As long as there is no early water breakthrough, the earlier the energy is supplied to the gas reservoirs, the better the gas recovery. On the other hand, for the systems having a small or moderate aquifer size and short distance between the two wells, performing water dumpflood when the rate is close to the economic rate is better because water is more likely to cause an early breakthrough when the distance is short. Water dumpflood into multiple nearly-depleted or depleted gas reservoirs is a novel study. The idea of using water dumpflood to increase gas recovery has been mentioned in the literature but has never been investigated. This detailed study will help a practicing engineer to understand the benefits of such method and can implement it with minimum cost and risk.

Keywords: dumpflood, increase gas recovery, low-pressure gas reservoir, multiple gas reservoirs

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Authors: Getalem E. Haylia

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The Ribb dam is one of the irrigation projects in the Upper Blue Nile basin, Ethiopia, to irrigate the Fogera plain. Reservoir sedimentation is a major problem because it reduces the useful reservoir capacity by the accumulation of sediments coming from the watersheds. Estimates of sediment yield are needed for studies of reservoir sedimentation and planning of soil and water conservation measures. The objective of this study was to simulate the Ribb dam catchment sediment yield using SWAT model and to estimate Ribb reservoir effective life according to trap efficiency methods. The Ribb dam catchment is found in North Western part of Ethiopia highlands, and it belongs to the upper Blue Nile and Lake Tana basins. Soil and Water Assessment Tool (SWAT) was selected to simulate flow and sediment yield in the Ribb dam catchment. The model sensitivity, calibration, and validation analysis at Ambo Bahir site were performed with Sequential Uncertainty Fitting (SUFI-2). The flow data at this site was obtained by transforming the Lower Ribb gauge station (2002-2013) flow data using Area Ratio Method. The sediment load was derived based on the sediment concentration yield curve of Ambo site. Stream flow results showed that the Nash-Sutcliffe efficiency coefficient (NSE) was 0.81 and the coefficient of determination (R²) was 0.86 in calibration period (2004-2010) and, 0.74 and 0.77 in validation period (2011-2013), respectively. Using the same periods, the NS and R² for the sediment load calibration were 0.85 and 0.79 and, for the validation, it became 0.83 and 0.78, respectively. The simulated average daily flow rate and sediment yield generated from Ribb dam watershed were 3.38 m³/s and 1772.96 tons/km²/yr, respectively. The effective life of Ribb reservoir was estimated using the developed empirical methods of the Brune (1953), Churchill (1948) and Brown (1958) methods and found to be 30, 38 and 29 years respectively. To conclude, massive sediment comes from the steep slope agricultural areas, and approximately 98-100% of this incoming annual sediment loads have been trapped by the Ribb reservoir. In Ribb catchment, as well as reservoir systematic and thorough consideration of technical, social, environmental, and catchment managements and practices should be made to lengthen the useful life of Ribb reservoir.

Keywords: catchment, reservoir effective life, reservoir sedimentation, Ribb, sediment yield, SWAT model

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

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Authors: A. Ghadbane, M. N. Bouaziz, S. Hanini, B. Baggoura, M. Abbaci

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The spacer grids are used to fix the rods bundle in a nuclear reactor core also act as turbulence-enhancing devices to improve the heat transfer from the hot surfaces of the rods to the surrounding coolant stream. Therefore, the investigation of thermal-hydraulic characteristics inside the rod bundles is important for optima design and safety operation of a nuclear reactor power plant. This contribution presents a feasibility study to use the ribbed spacer grids as mixing devices. The present study evaluates the effects of different ribbed spacer grids configurations on flow pattern and heat transfer in the downstream of the mixing devices in a 2 x 2 rod bundle array. This is done by obtaining velocity and pressure fields, turbulent intensity and the heat transfer coefficient using a three-dimensional CFD analysis. Numerical calculations are performed by employing K-ε turbulent model. The computational results obtained are promising and the comparison with standard spacer grids shows a clear difference which required the experimental approach to validate.

Keywords: PWR fuel assembly, spacer grid, mixing vane, swirl flow, turbulent heat transfer, CFD

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Authors: D. M. Rey, V. Delgado, J. Zambrano Nájera

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Increases in urbanization during XX century, has produced changes in natural dynamics of the basins, which has resulted in increases in runoff volumes, peak flows and flow velocities, that in turn increases flood risk. Higher runoff volumes decrease sewerage networks hydraulic capacity and can cause its failure. This in turn generates increasingly recurrent floods causing mobility problems and general economic detriment in the cities. In Latin America, especially Colombia, this is a major problem because urban population at late XX century was more than 70% is in urban areas increasing approximately in 790% in 1940-1990 period. Besides, high slopes product of Andean topography and high precipitation typical of tropical climates increases velocities and volumes even more, causing stopping of cities during storms. Thus, it becomes very important to know hydrological behavior of Andean Urban Watersheds. This research aims to determine the impact of urbanization in high sloped urban watersheds in its hydrology. To this end, it will be used as study area experimental urban watershed named Palogrande-San Luis watershed, located in the city of Manizales, Colombia. Manizales is a city in central western Colombia, located in Colombian Central Mountain Range (part of Los Andes Mountains) with an abrupt topography (average altitude is 2.153 m). The climate in Manizales is quite uniform, but due to its high altitude it presents high precipitations (1.545 mm/year average) with high humidity (83% average). It was applied HEC-HMS Hydrologic model on the watershed. The inputs to the model were derived from Geographic Information Systems (GIS) theme layers of the Instituto de Estudios Ambientales –IDEA of Universidad Nacional de Colombia, Manizales (Institute of Environmental Studies) and aerial photography taken for the research in conjunction with available literature and look up tables. Rainfall data from a network of 4 rain gages and historical stream flow data were used to calibrate and validate runoff depth using the hydrologic model. Manual calibration was made, and the simulation results show that the model selected is able to characterize the runoff response of the watershed due to land use for urbanization in high mountain watersheds.

Keywords: Andean watersheds modelling, high mountain urban hydrology, urban planning, hydrologic modelling

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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Authors: Andra-Iulia Ursa

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James Joyce employs several unconventional stylistic features in this landmark novel meant to experiment with language. The episode known as “Sirens” is entirely conceived around music and linguistic structures subordinated to sound. However, the aspiration to the condition of music is reflected throughout this entire literary work, as musical effects are echoed systematically. The numerous melodies scattered across the narrative play an important role in enhancing the thoughts and feelings that pass through the minds of the characters. Often the lyrics are distorted or interweaved with other words, preoccupations or memories, intensifying the stylistic effect. The Victorian song “Love’s old sweet song” is one of the most commonly referred to and meaningful musical allusions in Ulysses, becoming a leitmotif of infidelity. The lyrics of the song “M’appari”, from the opera “Martha”, are compared to an event from Molly and Bloom’s romantic history. Moreover, repeated phrases using words from “The bloom is on the rye” or “The croppy boy” serve as glances into the minds of the characters. Therefore, the central purpose of this study is to shed light on the way musical allusions flit through the episodes from the point of view of the stream of consciousness technique and to compare and analyse how these constructions are rendered into Romanian. Mircea Ivănescu, the single Romanian translator who succeeded in carrying out the translation of the entire ‘stylistic odyssey’, received both praises and disapprovals from the critics. This paper is not meant to call forth eventual flaws of the Romanian translation, but rather to elaborate the complexity of the task. Following an attentive examination and analysis of the two texts, from the point of view of form and meaning of the references to various songs, the conclusions of this study will be able to point out the intricacies of the process of translation.

Keywords: Joyce, melodious constructions, stream of consciousness, style, translation

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Authors: Ghada Soltane

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The objective of this study was to identify factors impacting supply chain collaboration. a quantitative study was carried out on a sample of 84 Tunisian industrial companies. To verify the research hypotheses and test the direct effect of these factors on supply chain collaboration a multiple regression method was used using SPSS 26 software. The results show that there are four factors direct effects that affect supply chain collaboration in a meaningful and positive way, including: trust, engagement, information sharing and information quality

Keywords: supply chain collaboration, factors of collaboration, principal component analysis, multiple regression

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Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

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Authors: Chin-Huang Huang, Chun-Chu Yeh

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Physical activities have a positive impact on individuals’ health and well-being. They also play an important role in promoting quality of life (QoL). The distance running enhances participants’ life satisfaction and provides positive experiences in physical activity. This study aims to measure the perception of QoL and to find the effect on satisfaction and intent to return for distance runners. Exploratory factor analysis is carried out to extract four major factorial dimensions of QoL, including multiple functions, spiritual, physical and cognitive factors. The main factors of QoL will be introduced into the regression function on satisfaction and return intention. The results show that the QoL factors including multiple functions, spiritual, physical and cognitive factors have a positive and significant impact on satisfaction for participants. The multiple functions and physical factors are also significantly positively correlated to the intent of return for runners.

Keywords: quality of life, physical activity, distance running, satisfaction

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Authors: Zhaopeng Xuan, Yuan Bian, C. Cailleaux, Jing Qin, S. Traore

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Traditional software engineering allows engineers to propose to their clients multiple specialized software distributions assembled from a shared set of software assets. The management of these assets however requires a trade-off between client satisfaction and software engineering process. Clients have more and more difficult to find a distribution or components based on their needs from all of distributed repositories. This paper proposes a software engineering for a user-driven software product line in which engineers define a feature model but users drive the actual software distribution on demand. This approach makes the user become final actor as a release manager in software engineering process, increasing user product satisfaction and simplifying user operations to find required components. In addition, it provides a way for engineers to manage and assembly large software families. As a proof of concept, a user-driven software product line is implemented for eclipse, an integrated development environment. An eclipse feature model is defined, which is exposed to users on a cloud-based built platform from which clients can download individualized Eclipse distributions.

Keywords: software product line, model-driven development, reverse engineering and refactoring, agile method

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Authors: Aguasanta Miguel Sarmiento, José Miguel Dávila, María Luisa de la Torre

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Acid mine drainage (AMD) is an acidic leachate with high levels of metals and sulphates in solution, which seriously affects the durability and strength of metallic materials used in the construction of structural and mechanical components. This paper presents the results of the evolution over time of the reduction in tensile strength and defects in AISI 304 stainless steel in contact with acid mine drainage. For this purpose, a total of 30 bars with a diameter of 8 mm and a length of 14 cm were placed transversely in the course of a stream contaminated by AMD from the sulphide mines of the Iberian Pyritic Belt (SW Spain). This stream has average pH values of 2.6, a potential of 660 mV and average concentrations of 12 g/L of sulphates, 1.2 g/L of Fe, 191 mg/L of Zn, etc. Every two months of exposure, 6 stainless steel bars were extracted from the acid stream. They were subjected to surface roughness analysis carried out with the help of Mitutoyo Surftest SJ-210 surface roughness tester. The analysis was carried out at three different points on 5 specimens from each series. The average reading of each parameter is calculated in order to ensure the accuracy of the measurements and the surface coverage. Arithmetic mean roughness value (Ra), mean roughness depth (Rz) and root mean square roughness (Rq) were measured. Five specimens from each series were statically tensile tested using universal equipment (Servosis ME 403 of 200kN). The specimens were clamped at their ends with two grips for cylindrical sections and the tensile force was applied at a constant speed of 0.5 kN/s, according to the requirements of standard UNE-EN ISO 6892-1: 2020. To determine the modulus of elasticity, limits close to 15% and 55% of the maximum load were used, depending on the course of each test. Field Emission Scanning Electron Microscopy (FESEM) was used to observe corrosion products and defects generated by exposure to AMD. Energy dispersive X-ray spectrometry (EDS) was used to analyse the chemical composition of the corrosion products formed. For this purpose, small pieces were cut from the resulting specimens, cleaned and embedded in epoxy resin. The results show that after only 5 months of exposure of AISI 304 stainless steel to the mining environment, the surface roughness increases significantly, with average depths almost 6 times greater than the initial one. Cracks are observed on the surface of the material, which increase in size with the time of exposure. A large number of grains with a composition of more than 57% Pb and 16% Sn can be observed inside these cracks. Tensile tests show a reduction in the resistance of this material after only two months of exposure. The results show the serious problems that would result from the use of this material for the use of mechanical components in a sulphide mining environment, not only because of the significant reduction in the lifetime of such components, but also because of the implications for human safety.

Keywords: acid mine drainage, corrosion, mechanical properties, stainless steel

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Authors: Jui-Ching Chou

Abstract:

Numerical simulation is a popular method used to evaluate the effects of soil liquefaction on a structure or the effectiveness of a mitigation plan. Many constitutive models (UBCSAND model, PM4 model, SANISAND model, etc.) were presented to model the liquefaction phenomenon. In general, inputs of a constitutive model need to be calibrated against the soil cyclic resistance before being applied to the numerical simulation model. Then, simulation results can be compared with results from simplified liquefaction potential assessing methods. In this article, inputs of the UBCSAND model, a simple elastic-plastic stress-strain model, are calibrated against several popular generic liquefaction triggering curves of simplified liquefaction potential assessing methods via FLAC program. Calibrated inputs can provide engineers to perform a preliminary evaluation of an existing structure or a new design project.

Keywords: calibration, liquefaction, numerical simulation, UBCSAND Model

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Authors: I. Benhsine, M. Hellou, F. Lominé, Y. Roques

Abstract:

During the manufacture of fertilizer, it is necessary to add water for granulation purposes. The water content is then removed or reduced using rotary dryers. They are commonly used to dry wet granular materials and they are usually fitted with lifting flights. The transport of granular materials occurs when particles cascade from the lifting flights and fall into the air stream. Each cascade consists of a lifting and a falling cycle. Lifting flights are thus of great importance for the transport of granular materials along the dryer. They also enhance the contact between solid particles and the air stream. Optimization of the drying process needs an understanding of the behavior of granular materials inside a rotary dryer. Different approaches exist to study the movement of granular materials inside the dryer. Most common of them are based on empirical formulations or on study the movement of the bulk material. In the present work, we are interested in the behavior of each particle in the cross section of the dryer using Discrete Element Method (DEM) to understand. In this paper, we focus on studying the hold-up, the cascade patterns, the falling time and the falling length of the particles leaving the flights. We will be using two segment flights. Three different profiles are used: a straight flight (180° between both segments), an angled flight (with an angle of 150°), and a right-angled flight (90°). The profile of the flight affects significantly the movement of the particles in the dryer. Changing the flight angle changes the flight capacity which leads to different discharging profile of the flight, thus affecting the hold-up in the flight. When the angle of the flight is reduced, the range of the discharge angle increases leading to a more uniformed cascade pattern in time. The falling length and the falling time of the particles also increase up to a maximum value then they start decreasing. Moreover, the results show an increase in the falling length and the falling time up to 70% and 50%, respectively, when using a right-angled flight instead of a straight one.

Keywords: discrete element method, granular materials, lifting flight, rotary dryer

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Authors: Rohaya Abdul Wahab, Raja Mohd Fuad Tengku Aziz, Nazaliza Othman, Sharifah Saleh, Nabihah Razali, Muhammad Al Baqir Zinal Abidin, Md Hanif Md Nasir

Abstract:

This paper will discuss how we optimize our physical verification flow in our IC Design Department having various rule decks from multiple foundries. Our ultimate goal is to achieve faster time to tape-out and avoid schedule delay. Currently the physical verification runtimes and memory usage have drastically increased with the increasing number of design rules, design complexity and the size of the chips to be verified. To manage design violations, we use a number of solutions to reduce the amount of violations needed to be checked by physical verification engineers. The most important functions in physical verifications are DRC (design rule check), LVS (layout vs. schematic) and XRC (extraction). Since we have a multiple number of foundries for our design tape-outs, we need a flow that improve the overall turnaround time and ease of use of the physical verification process. The demand for fast turnaround time is even more critical since the physical design is the last stage before sending the layout to the foundries.

Keywords: physical verification, DRC, LVS, XRC, flow, foundry, runset

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Authors: Simin Eydivand, Ali Ghanadieslami, Reza Amiri

Abstract:

Natural gas liquids (NGLs) are light hydrocarbons that are dissolved in associated or non‐associated natural gas in a hydrocarbon reservoir and are produced within a gas stream. There are different ways to calculate the price of NGL. In this study, a spreadsheet calculation method is used for calculation of NGL price with an attractive economy of IRR 25%. For a typical NGL Plant with 3,200,000 t/y capacity of investment and operation of 90% capacity to have IRR 25%, the price of NGL is calculated 277 $/t.

Keywords: natural gas liquid, NGL, LPG, price, NGL fractionation, NF, investment, IRR, NPV

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Authors: Sulemana Saibu, Moses Ikpeme

Abstract:

Background: The severity of the COVID-19 pandemic, brought on by the SARS-CoV-2 coronavirus, has been unprecedented since the 1918 influenza pandemic. SARS-CoV-2 cases of CNS and peripheral nervous system disease, including neurodegenerative disorders and chronic immune-mediated diseases, may be anticipated based on knowledge of past coronaviruses, particularly those that caused the severe acute respiratory syndrome and Middle East respiratory syndrome outbreaks. Although respiratory symptoms are the most common clinical presentation, neurological symptoms are becoming increasingly recognized, raising concerns about their potential role in causing Parkinson's disease, Multiple sclerosis, and Narcolepsy. This systematic review aims to summarize the current evidence by exploring the association between COVID-19 infection and how it may overlap with etiological mechanisms resulting in Narcolepsy, Parkinson's disease, and Multiple sclerosis. Methods: A systematic search was conducted using electronic databases ((PubMed/MedLine, Embase, PsycINFO, ScieLO, Web of Science, ProQuest (Biotechnology, Virology, and AIDS), Scopus, and CINAHL)) to identify studies published between January 2020 and December 2022 that investigated the association between COVID-19 and Parkinson's disease, multiple sclerosis, and Narcolepsy. Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review was performed and reported. Study quality was assessed using the Critical Appraisal Skills Programme Checklist and the Joanna Briggs Institute Critical appraisal tools. Results: A total of 21 studies out of 1025 met the inclusion criteria, including 8 studies reporting Parkinson's disease, 11 on multiple sclerosis, and 2 on Narcolepsy. In COVID-19 individuals compared to the general population, Narcolepsy, Parkinson's disease, and multiple sclerosis were shown to have a higher incidence. The findings imply that COVID-19 may worsen the signs or induce multiple sclerosis and Parkinson's disease and may raise the risk of developing Narcolepsy. Further research is required to confirm these connections because the available data is insufficient. Conclusion: According to the existing data, COVID-19 may raise the risk of Narcolepsy and have a causative relationship with Parkinson's disease, multiple sclerosis, and other diseases. More study is required to confirm these correlations and pinpoint probable mechanisms behind these interactions. Clinicians should be aware of how COVID-19 may affect various neurological illnesses and should treat patients who are affected accordingly.

Keywords: COVID-19, parkinson’s disease, multiple sclerosis, narcolepsy, neurological disorders, sars-cov-2, neurodegenerative disorders, chronic immune-mediated diseases

Procedia PDF Downloads 81