Search results for: impact models

Authors: Imran Badshah

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This paper study the impact of Un-signalized pedestrian on traffic flow at Stationary Bottleneck. The Highway Capacity Manual (HCM) analyze the methodology of level of service for Urban street segment but it does not include the impact of un-signalized pedestrian crossing at stationary bottleneck. The un-signalized pedestrian crossing in urban road segment causes conflict between vehicles and pedestrians. As a result, the average time taken by vehicle to travel along a road segment increased. The speed of vehicle and the level of service decreases as the running time of a segment increased. To analyze the delay, we need to determine the pedestrian speed while crossing the road at a stationary bottleneck. The objective of this research is to determine the speed of pedestrian and its impact on traffic flow at stationary bottleneck. In addition, the result of this study should be incorporated in the Urban Street Analysis Chapter of HCM.

Keywords: stationary bottleneck, traffic flow, pedestrian speed, HCM

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Authors: Mohsin Aslam Khan

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Decision making tendency varies in people with different socio demographics. This study was conducted to identify the impact of work experience on decisional conflict and whether there is a gender differences in decisional conflict. Convenience sampling was more appropriate for this exploratory research. AM O’ Connor decisional conflict scale, (1995) with cronbach alpha 0.900 was administered on sample size of 109 participants (62males, 47females). The responses were scored according to the AM O’ Connor decisional conflict scale manual, (1995). The results of the study indicate that work experience has no significant impact on decisional conflict, whereas gender differences in decisional conflict illustrates significant mean score differences among male and female participants.

Keywords: decision making, decisional conflict, gender decision making, work experience

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Authors: Leonardo C. Pacheco-Londoño, Nataly J Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta-Hoyos, Elkin Navarro, Gustavo Aroca-Martinez, Karin Rondón-Payares, Alberto C. Espinosa-Garavito, Samuel P. Hernández-Rivera

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At the Life Science Research Center at Simon Bolivar University, a primary focus is the diagnosis of various diseases, and the use of gold nanoparticles (Au-NPs) in diverse biomedical applications is continually expanding. In the present study, Au-NPs were employed as substrates for Surface-Enhanced Raman Spectroscopy (SERS) aimed at diagnosing kidney diseases arising from Lupus Nephritis (LN), preeclampsia (PC), and Hypertension (H). Discrimination models were developed for distinguishing patients with and without kidney diseases based on the SERS signals from urine samples by partial least squares-discriminant analysis (PLS-DA). A comparative study of the Raman signals across the three conditions was conducted, leading to the identification of potential metabolite signals. Model performance was assessed through cross-validation and external validation, determining parameters like sensitivity and specificity. Additionally, a secondary analysis was performed using machine learning (ML) models, wherein different ML algorithms were evaluated for their efficiency. Models’ validation was carried out using cross-validation and external validation, and other parameters were determined, such as sensitivity and specificity; the models showed average values of 0.9 for both parameters. Additionally, it is not possible to highlight this collaborative effort involved two university research centers and two healthcare institutions, ensuring ethical treatment and informed consent of patient samples.

Keywords: SERS, Raman, PLS-DA, kidney diseases

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

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

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

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Authors: Shreedevi Moharana, Subashisa Dutta

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The present study focuses on the estimation of crop biophysical parameters like crop chlorophyll, nitrogen and water stress at plot scale in the crop fields. To achieve these, we have used high-resolution satellite LISS IV imagery. A new methodology has proposed in this research work, the spectral shape function of paddy crop is employed to get the significant wavelengths sensitive to paddy crop parameters. From the shape functions, regression index models were established for the critical wavelength with minimum and maximum wavelengths of multi-spectrum high-resolution LISS IV data. Moreover, the functional relationships were utilized to develop the index models. From these index models crop, biophysical parameters were estimated and mapped from LISS IV imagery at plot scale in crop field level. The result showed that the nitrogen content of the paddy crop varied from 2-8%, chlorophyll from 1.5-9% and water content variation observed from 40-90% respectively. It was observed that the variability in rice agriculture system in India was purely a function of field topography.

Keywords: crop parameters, index model, LISS IV imagery, plot scale, shape function

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Authors: Zh. Bolatova, Zh. Bulkhairova, M. Kulshigashova

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In this article, the authors consider the main aspects of climate change's impact on the economic efficiency of leguminous crop production and perspectives in Kazakhstan. It is worth noting that climate change has an impact on the instability of leguminous crops and leads to a decrease in production efficiency. Ultimately, all of the above determines the relevance and significance of this topic. The level of productivity of grain and legumes in the country and by regions of Kazakhstan was also analyzed. The authors conducted a survey and a deeper analysis of agricultural producers in the Kazakhstan region. In the end, the authors considered the prospects for the development of leguminous crops in Kazakhstan. For the article have been used different literature and reports from IPCC, WMO, WTO, FAO, UNEP, UNFCCC, UNDP, IMF, WB, OECD, KAZHYDROMET, Committee of the Statistics of Kazakhstan, etc.

Keywords: climate change, economic efficiency, leguminous crops, production, yield

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Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

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Authors: A. Ali, V. Strezov, P. Davies, I. Wright, T. Kan

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The environmental impacts arising from mining activities affect the air, water, and soil quality. Impacts may result in unexpected and adverse environmental outcomes. This study reports on the impact of coal production on sediment in Sydney region of Australia. The sediment samples upstream and downstream from the discharge points from three mines were taken, and 80 parameters were tested. The results were assessed against sediment quality based on presence of metals. The study revealed the increment of metal content in the sediment downstream of the reference locations. In many cases, the sediment was above the Australia and New Zealand Environment Conservation Council and international sediment quality guidelines value (SQGV). The major outliers to the guidelines were nickel (Ni) and zinc (Zn).

Keywords: coal mine, environmental impact, produced water, sediment quality guidelines value (SQGV)

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Thu Le Thi Bao

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Industrial parks have both active roles in promoting economic development and source of appearance of boarding houses and slums in the adjacent area, lacking infrastructure, causing many social evils. The context of the recent pandemic and climate change on a global scale pose issues that need to be resolved for sustainable development. Ho Chi Minh City aims to develop housing for migrant workers to stabilize human resources and, at the same time, solve problems of social evils caused by poor living conditions. The paper focuses on the content of renovating existing industrial parks and worker accommodation in Ho Chi Minh City to propose appropriate models, contributing to the goal of urban embellishment and solutions for industrial parks to adapt to abnormal impact conditions such as pandemics, climate change, crises.

Keywords: industrial park, social housing, accommodation, distribution center

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Wagner Pereira, Alphonse Kelecom

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Mining can impact the environment, and the major impact of some mining activities is the radiological impact. In human populations, such impact is well studied and regulated. For biota, this assessment always had as focus the protection of human food chain. The protection of biota itself is a new approach, still developing. In order to contribute to this new approach, fish collecting was carried out in areas of naturally occurring radioactive materials (NORM), where a uranium mine is in decommissioning phase. The activity concentrations were analyzed, in Bq/kg wet weight, for Uranium (Unat), Th-232 and Ra-226 in the lambari fish Astyanax bimaculatus L. (omnivorous fish) and in the traíra fish Hoplias malabaricus Bloch, 1794 (carnivorous fish). Seven composite samples (that is: a sufficient number of individuals to reach at least 2 kg of fresh weight) were collected every six months between 2013 and 2015. The mean activity concentrations (AC) for uranium ranged from 1.12 (lambari) to 0.60 (lungfish). For Th, variations ranged from 0.30 to 0.05 (lambari and traíra, respectively). Finally, the Ra-226 means ranged between 0.08 and 0.03. No temporal trends of accumulation could be identified. Systematically, the AC values of radionuclides were higher in omnivorous fish when compared to the carnivore ones.

Keywords: biota dose, NORM, fish, environmental protection

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Authors: Sara El Mansouria Beghdadi

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Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

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Authors: Aradhna Pandarum

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South Africa is experiencing an exponential growth of distributed solar PV installations. This is due to various factors with the predominant one being increasing electricity tariffs along with decreasing installation costs, resulting in attractive business cases to some end-users. Despite there being a variety of economic and environmental advantages associated with the installation of PV, their potential impact on distribution grids has yet to be thoroughly investigated. This is especially true since the locations of these units cannot be controlled by Network Service Providers (NSPs) and their output power is stochastic and non-dispatchable. This report details two case studies that were completed to determine the possible voltage and technical losses impact of increasing PV penetration in the Northern Cape of South Africa. Some major impacts considered for the simulations were ramping of PV generation due to intermittency caused by moving clouds, the size and overall hosting capacity and the location of the systems. The main finding is that the technical impact is different on a constrained feeder vs a non-constrained feeder. The acceptable PV penetration level is much lower for a constrained feeder than a non-constrained feeder, depending on where the systems are located.

Keywords: medium voltage networks, power system losses, power system voltage, solar photovoltaic

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Authors: Ricardo Merlo

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Attitude is a hypothetical construct that can be significantly measured to know the favorable or unfavorable predisposition that students have towards the teaching of sciences such as Physics. Although the state-of-the-art attitude test used in Physics teaching indicated different design and validation models in different groups of students, the analysis of the weight given to each dimension that supported the attitude was scarcely evaluated. Then, in this work, a methodology of attitude questionnaire construction process was proposed that allowed the teacher to design and validate the measurement instrument for different subjects of Physics at the university level developed in the classroom according to the weight considered to the affective, knowledge, and behavioural dimensions. Finally, questionnaire models were tested for the case of incoming university students, achieving significant results in the improvement of Physics teaching.

Keywords: attitude, physics teaching, motivation, academic performance

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Authors: Olanrewaju Johnson Akinduntire

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This paper investigates the statistical evaluation of books, citations, articles, journals and other publications in accounting and finance on leveraging technology to mitigate the impact of COVID-19 on business. The research proffers an appraisal of the impact of computerized accounting systems in pre and post pandemic era on activities of the formal and informal sectors, it analyzes the concept of computerized accounting systems, and it seeks to determine the impact of computerized of the overall activities of the informal sector. A special focus of this ICT strategy should be to demystify and promote the diffusion of ICT as a general-purpose technology to the informal sector. It is believed that the use of new technologies can be crucial to meeting the Millennium Development Goals (MDGs) in a timely and effective fashion. Consequent to these, there is a need to prevent the further marginalization of the informal sector by availing ICT services which are mixed appropriately and also properly located. By implication, this will help them access markets and other business information, which can enable or make their economic activities more vibrant and facilitate the availability of information about new opportunities. Conclusively, for one to understand the application of ICT and their locational dynamics in informal sector clusters, there is a need to comprehend and acknowledge the drivers and pressures leading to the adoption of new technology.

Keywords: COVID-19 , (MDGs) , ICT, bibliometric

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Authors: Jiawei Chen, Jia Qu, Dianwei Ju

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316L stainless steel has good mechanical and technological properties, has been widely used in shipbuilding and aerospace manufacturing. In order to understand the effect of strain rate on the yield limit of 316L stainless steel and the constitutive relationship of the materials at different strain rates, this paper used the INSTRON-4505 electronic universal testing machine to study the mechanical properties of the tensile specimen under quasi-static conditions. Meanwhile, the Zwick-Roell RKP450 intelligent oscillometric impact tester was used to test the tensile specimens at different strain rates. Through the above two kinds of experimental researches, the relationship between the true stress-strain and the engineering stress-strain at different strain rates is obtained. The result shows that the tensile yield point of 316L stainless steel increases with the increase of strain rate, and the real stress-strain curve of the 316L stainless steel has a better normalization than that of the engineering stress-strain curve. The real stress-strain curves can be used in the practical engineering of impact stretch to improve its safety.

Keywords: impact stretch, 316L stainless steel, strain rate, real stress-strain, normalization

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Authors: Holly A. Rick, Melissa McCartney

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The role of faculty has changed from the impact of Covid-19. Universities are changing faculty expectations. There is a changes in faculty workloads, and shift in how faculty work within a university. The research will identify areas where faculty are satisfied with their work, areas they would like their organizations to change, and how the faculty life is impacted by outside university obligations. A survey to obtain work-life balance, teaching responsibilities, and how a faculty’s personal life impacts their ability to work at their organization was conducted. The results of this research will identify areas where faculty have opportunities to engage in teaching, to balance their work life, and where organizations can change to support their faculty. Different ways of teaching including hyflex and other multimodal models will allow for faculty to engage in their teaching practice, professional development, and begin to establish work-life balance activities.

Keywords: faculty engagement, faculty responsibilities, HyFlex, teaching, work-life balance

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Authors: Mohammad Mahdi Mozaffari, Mehdi Ajalli, Delaram Jafargholi

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The main purpose of this study is to investigate the impact of the system of ERP (Enterprise Resource Planning) and SCM (Supply Chain Management) on the competitive advantage and performance of Mamot Company. The methods for collecting information in this study are library studies and field research. A questionnaire was used to collect the data needed to determine the relationship between the variables of the research. This questionnaire contains 38 questions. The direction of the current research is applied. The statistical population of this study consists of managers and experts who are familiar with the SCM system and ERP. Number of statistical society is 210. The sampling method is simple in this research. The sample size is 136 people. Also, among the distributed questionnaires, Reliability of the Cronbach's Alpha Cronbach's Questionnaire is evaluated and its value is more than 70%. Therefore, it confirms reliability. And formal validity has been used to determine the validity of the questionnaire, and the validity of the questionnaire is confirmed by the fact that the score of the impact is greater than 1.5. In the present study, one variable analysis was used for central indicators, dispersion and deviation from symmetry, and a general picture of the society was obtained. Also, two variables were analyzed to test the hypotheses; measure the correlation coefficient between variables using structural equations, SPSS software was used. Finally, multivariate analysis was used with statistical techniques related to the SPLS structural equations to determine the effects of independent variables on the dependent variables of the research to determine the structural relationships between the variables. The results of the test of research hypotheses indicate that: 1. Supply chain management practices have a positive impact on the competitive advantage of the Mammoth industrial complex. 2. Supply chain management practices have a positive impact on the performance of the Mammoth industrial complex. 3. Planning system Organizational resources have a positive impact on the performance of the Mammoth industrial complex. 4. The system of enterprise resource planning has a positive impact on Mamot's competitive advantage. 5.The competitive advantage has a positive impact on the performance of the Mammoth industrial complex 6.The system of enterprise resource planning Mamot Industrial Complex Supply Chain Management has a positive impact. The above results indicate that the system of enterprise resource planning and supply chain management has an impact on the competitive advantage and corporate performance of Mamot Company.

Keywords: enterprise resource planning, supply chain management, competitive advantage, Mamot company performance

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: Vinayak Malaghan, Digvijay Pawar

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Speed is the independent parameter which plays a vital role in the highway design. Design consistency of the highways is checked based on the variation in the operating speed. Often the design consistency fails to meet the driver’s expectation which results in the difference between operating and design speed. Literature reviews have shown that significant crashes take place in horizontal curves due to lack of design consistency. The paper focuses on continuous speed profile study on tangent to curve transition for both day and night daytime. Data is collected using GPS device which gives continuous speed profile and other parameters such as acceleration, deceleration were analyzed along with Tangent to Curve Transition. In this present study, models were developed to predict operating speed on tangents and horizontal curves as well as model indicating the speed reduction from tangent to curve based on continuous speed profile data. It is observed from the study that vehicle tends to decelerate from approach tangent to between beginning of the curve and midpoint of the curve and then accelerates from curve to tangent transition. The models generated were compared for both day and night and can be used in the road safety improvement by evaluating the geometric design consistency.

Keywords: operating speed, design consistency, continuous speed profile data, day and night time

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Authors: Daniel Zarzo Montes, Manuel Zarzo Castelló

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Proteins play a fundamental role in biology, but their structure is complex, and it is a challenge for teachers to conceptually explain the differences between their primary, secondary, tertiary, and quaternary structures. On the other hand, there are currently many computer programs to visualize the 3D structure of proteins, but they require advanced training and knowledge. Moreover, it becomes difficult to visualize the sequence of amino acids in these models, and how the protein conformation is reached. Given this drawback, a simple and instructive procedure is proposed in order to teach the protein structure to undergraduate and graduate students. For this purpose, insulin has been chosen because it is a protein that consists of 51 amino acids, a relatively small number. The methodology has consisted of the use of plastic atom models, which are frequently used in organic chemistry and biochemistry to explain the chirality of biomolecules. For didactic purposes, when the aim is to teach the biochemical foundations of proteins, a manipulative system seems convenient, starting from the chemical structure of amino acids. It has the advantage that the bonds between amino acids can be conveniently rotated, following the pattern marked by the 3D models. First, the 51 amino acids were modeled, and then they were linked according to the sequence of this protein. Next, the three disulfide bonds that characterize the stability of insulin have been established, and then the alpha-helix structure has been formed. In order to reach the tertiary 3D conformation of this protein, different interactive models available on the Internet have been visualized. In conclusion, the proposed methodology seems very suitable for biology and biochemistry students because they can learn the fundamentals of protein modeling by means of a manipulative procedure as a basis for understanding the functionality of proteins. This methodology would be conveniently useful for a biology or biochemistry laboratory practice, either at the pre-graduate or university level.

Keywords: protein structure, 3D model, insulin, biomolecule

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Authors: H. Al-Hajeri

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The computation of the effectiveness of turbulence enhancement surface features, such as ribs as means of promoting mixing and hence heat transfer, has attracted the continued attention of the engineering community. In this study, the simulation of a three-dimensional cooling passage is carried out employing a number of turbulence models including Durbin model. The cooling passage consists of a square section duct whose upper and lower surfaces feature staggered cuboid ribs. The main objective of this paper is to provide comparisons of the performance of the v2-f model against other established turbulence models as implemented in the commercial CFD code Ansys Fluent. The present study demonstrates that the v2-f model can successfully capture the isothermal air flow phenomena in flow over obstacles.

Keywords: CFD, cooling passage, Durbin model, turbulence model

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Authors: J. Y. Heo, H. G. Sung

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Numerical simulations have been performed for assessment of compressibility correction of two-equation turbulence models suitable for large scale separation flows perturbed by pintle strokes. In order to take into account pintle movement, a sliding mesh method was applied. The chamber pressure, mass flow rate, and thrust have been analyzed, and the response lag and sensitivity at the chamber and nozzle were estimated for a movable pintle. The nozzle performance for pintle reciprocating as its insertion and extraction processes, were analyzed to better understand the dynamic performance of the pintle nozzle.

Keywords: pintle, sliding mesh, turbulent model, compressibility correction

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Beloulou Mohamed Lamine, Bouhouf Attef, Meliani Walid, Sellami Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with post-surgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariate analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature or component, particularly in surgeries that are more prone to chronicization.

Keywords: chronic postsurgical pain, postsurgical chronic neuropathic pain, regional anesthesia and analgesia techniques (RAAT), neuropathic pain score dn2, preventive impact

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Authors: Mohammadali Mokhtari

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Developing countries increasingly recognize the need to reduce energy subsidies. Cash transfers are proposed instead of subsidies to mitigate the negative effects of energy subsidy reforms. However, there is little evidence about the redistribution effect of these subsidy-to-cash reforms. We study the redistribution impact of the largest subsidy-to-cash reform in the history of developing countries in Iran in 2010. We find a strong pro-poor impact of the reform using five inequality measures, including the Gini index and the ratio of rich to poor expenditures. Finally, we show this pro-poor impact is on average and discuss other possible mechanisms by which low-income groups objected to these pro-poor reforms in the next round, which took place in November 2019 and led to wide and large protests.

Keywords: energy economics, subsidy reform, inequality, Middle East, Iran

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Authors: Vishal Das, Tianyi Mao, Zhicheng Geng, Carmen Flores, Diego Pelloso, Fang Wang

Abstract:

Accurate sell-in and sell-out forecasting is a ubiquitous problem in the retail industry. It is an important element of any demand planning activity. As a global food and beverage company, Nestlé has hundreds of products in each geographical location that they operate in. Each product has its sell-in and sell-out time series data, which are forecasted on a weekly and monthly scale for demand and financial planning. To address this challenge, Nestlé Chilein collaboration with Amazon Machine Learning Solutions Labhas developed their in-house solution of using machine learning models for forecasting. Similar products are combined together such that there is one model for each product category. In this way, the models learn from a larger set of data, and there are fewer models to maintain. The solution is scalable to all product categories and is developed to be flexible enough to include any new product or eliminate any existing product in a product category based on requirements. We show how we can use the machine learning development environment on Amazon Web Services (AWS) to explore a set of forecasting models and create business intelligence dashboards that can be used with the existing demand planning tools in Nestlé. We explored recent deep learning networks (DNN), which show promising results for a variety of time series forecasting problems. Specifically, we used a DeepAR autoregressive model that can group similar time series together and provide robust predictions. To further enhance the accuracy of the predictions and include domain-specific knowledge, we designed an ensemble approach using DeepAR and XGBoost regression model. As part of the ensemble approach, we interlinked the sell-out and sell-in information to ensure that a future sell-out influences the current sell-in predictions. Our approach outperforms the benchmark statistical models by more than 50%. The machine learning (ML) pipeline implemented in the cloud is currently being extended for other product categories and is getting adopted by other geomarkets.

Keywords: sell-in and sell-out forecasting, demand planning, DeepAR, retail, ensemble machine learning, time-series

Procedia PDF Downloads 274

Authors: Mostert Jacques, Gulseven Osman, Williams Courtney

Abstract:

The Covid-19 pandemic caused unparalleled disruption in the lives of the majority of the world. This included school closures and introduction of Online Learning. In this article we investigated the impact of distance learning on the self-efficacy and anxiety levels experienced by educators in South Africa. We surveyed 60 respondents from Independent Schools using a Likert Scale rating of 0 to 4. The results suggested that despite experiencing moderate anxiety, educators showed a sense of high self-efficacy during distance learning. This was specifically true for those with underlying health concerns. There was no significant difference between how the different genders experienced anxiety and self-efficacy. Further research into the impact on learners’ anxiety levels during distance learning will provide policymakers and educators with a better understanding of how the use of technology is influencing the effectiveness of teaching, learning, and assessment.

Keywords: COVID-19, education, self-efficacy, anxiety

Procedia PDF Downloads 205