Search results for: reliable facility location model

Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

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Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

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Authors: Azhan Azhar, Mohammed Sakib, Zaurez Ahmad

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The energy we receive from the sun is known as solar energy, and it is a reliable, long-lasting, eco-friendly and the most widely used energy source in the 21st century. It is. There are several techniques for harnessing solar energy, and we are all seeing large utility-scale projects to collect maximum amperes from the sun using current technologies. Solar PV is now on the rise as a means of harvesting energy from the sun. Moving a step further, our project is focused on designing an All-in-one portable Solar Energy based solution. We considered the minimum load conditions and evaluated the requirements of various devices utilized in this study to resolve the power requirements of small stores, hawkers, or travelers.

Keywords: DOD-depth of discharge, pulse width modulation charge controller, renewable energy, solar PV- solar photovoltaic

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Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

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Authors: Onyeke Paul Chuks

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Nigeria is ranked among the developing nations and a country with a low income per capita. The consequences of this economic situation have led to the low achievement records below UN benchmark especially in the area of basic education for her citizens. The country is, however, making relentless efforts at arresting the situation by making budgetary allocations to ensure the realization of Millennium Development Goal No. 2 which is achieving universal basic education, her distressed economy notwithstanding. Basic education which comprises primary and lower secondary education as well as pre-primary and/or adult literacy programs have suffered serious setbacks orchestrated by the dwindling of the nation’s economy. This category of education being the bedrock of all other levels of education is regarded as a priority by developing countries and also the focus of the Education for All Movement led by UNESCO. The introduction of child-friendly school model is one of the strategies designed by UNESCO to achieving this all important MDGs goal No. 2. Child-friendly education model is aimed at replacing the out-dated, mundane, regimented and officious school administrative model where the basic rights of school children are trampled upon with impunity and community participation in school activities is viewed as unnecessary interference by school managers. This paper ex-rayed the potential obstacles likely to impinge on the implementation of child-friendly school model in Nigeria especially from the angle of her distressed economy and the colossal effects of the corrupt practices bedeviling the nation. The paper as well outlines prospects for the successful implementation of the child-friendly school model in Nigeria.

Keywords: child-friendly school, distressed economy, model, Nigeria

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Authors: M. Dezyani, R. Ezzati, H. Mirzaei

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The objectives of this study were to determine the effect of pH on chemical, structural, and functional properties of Fetta cheese, and to relate changes in structure to changes in cheese unctionality. Fetta cheese was obtained from a cheese-production facility and stored at 4°C. Ten days after manufacture, the cheese was cut into blocks that were vacuum-packaged and stored for 4 d at 4°C. Cheese blocks were then high-pressure injected one, three, or five times with a 20% (wt/wt) glucono-δ-lactone solution. Successive injections were performed 24 h apart. Cheese blocks were then analyzed after 40 d of storage at 4°C. Acidulant injection decreased cheese pH from 5.3 in the uninjected cheese to 4.7 after five injections. Decreased pH increased the content of soluble calcium and slightly decreased the total calcium content of cheese. At the highest level, injection of acidulant promoted syneresis. Thus, after five injections, the moisture content of cheese decreased from 34 to 31%, which esulted in decreased cheese weight. Lowered cheese pH, 4.7 compared with 5.3, also resulted in contraction of the protein matrix. Acidulant injection decreased cheese hardness and cohesiveness, and the cheese became more crumbly.

Keywords: calcium, high-pressure injection, protein matrix, syneresis

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: Jiashu Shen

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Objectives: Spirometry is a basic reference for health evaluation which is widely used in clinical. Previous reference of spirometry is not applicable because of drastic changes of social and natural circumstance in China. A new reference values for the spirometry of the Chinese population is extremely needed. Method: Spirometric reference value was established using the statistical modeling method Generalized Additive Models for Location, Scale and Shape for forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, and maximal mid-expiratory flow (MMEF). Results: Data from 236,606 healthy non-smokers aged 4–90 years was collected from the MJ Health Check database. Spirometry equations for FEV1, FVC, MMEF, and FEV1/FVC were established, including the predicted values and lower limits of normal (LLNs) by sex. The predictive equations that were developed for the spirometric results elaborated the relationship between spirometry and age, and they eliminated the effects of height as a variable. Most previous predictive equations for Chinese spirometry were significantly overestimated (to be exact, with mean differences of 22.21% in FEV1 and 31.39% in FVC for males, along with differences of 26.93% in FEV1 and 35.76% in FVC for females) or underestimated (with mean differences of -5.81% in MMEF and -14.56% in FEV1/FVC for males, along with a difference of -14.54% in FEV1/FVC for females) the results of lung function measurements as found in this study. Through cross-validation, our equations were established as having good fit, and the means of the measured value and the estimated value were compared, with good results. Conclusions: Our study updates the spirometric reference equations for Chinese people of all ages and provides comprehensive values for both physical examination and clinical diagnosis.

Keywords: Chinese, GAMLSS model, reference values, spirometry

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Authors: Shailendra M. Pradhan, Upendra M. Pradhan

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Disasters are perennial features of human civilization. The recurring earthquakes, floods, cyclones, among others, that result in massive loss of lives and devastation, is a grim reminder of the fact that, despite all our success stories of development, and progress in science and technology, human society is perennially at risk to disasters. The apparent threat of climate change and global warming only severe our disaster risks. Darjeeling hills, situated along Eastern Himalayan region of India, and famous for its three Ts – tea, tourism and toy-train – is also equally notorious for its disasters. The recurring landslides and earthquakes, the cyclone Aila, and the Ambootia landslides, considered as the largest landslide in Asia, are strong evidence of the vulnerability of Darjeeling hills to natural disasters. Given its geographical location along the Hindu-Kush Himalayas, the region is marked by rugged topography, geo-physically unstable structure, high-seismicity, and fragile landscape, making it prone to disasters of different kinds and magnitudes. Most of the studies on disasters in Darjeeling hills are, however, scientific and geographical in orientation that focuses on the underlying geological and physical processes to the neglect of social and political conditions. This has created a tendency among the researchers and policy-makers to endorse and promote a particular type of discourse that does not consider the social and political aspects of disasters in Darjeeling hills. Disaster, this paper argues, is a complex phenomenon, and a result of diverse factors, both physical and human. The hazards caused by the physical and geological agents, and the vulnerabilities produced and rooted in political, economic, social and cultural structures of a society, together result in disasters. In this sense, disasters are as much a result of political and economic conditions as it is of physical environment. The human aspect of disasters, therefore, compels us to address intricating social and political challenges that ultimately determine our resilience and vulnerability to disasters. Set within the above milieu, the aims of the paper are twofold: a) to provide a political and sociological account of disasters in Darjeeling hills; and, b) to identify and address the root causes of its vulnerabilities to disasters. In situating disasters in Darjeeling Hills, the paper adopts the Pressure and Release Model (PAR) that provides a theoretical insight into the study of social and political aspects of disasters, and to examine myriads of other related issues therein. The PAR model conceptualises risk as a complex combination of vulnerabilities, on the one hand, and hazards, on the other. Disasters, within the PAR framework, occur when hazards interact with vulnerabilities. The root causes of vulnerability, in turn, could be traced to social and political structures such as legal definitions of rights, gender relations, and other ideological structures and processes. In this way, the PAR model helps the present study to identify and unpack the root causes of vulnerabilities and disasters in Darjeeling hills that have largely remained neglected in dominant discourses, thereby providing a more nuanced and sociologically sensitive understanding of disasters.

Keywords: Darjeeling, disasters, PAR, vulnerabilities

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Authors: K. Zehouani, I. Oldal

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Purpose: In the human knee joint, degenerative joint disease may happen with time. The standard treatment of this disease is the total knee replacement through prosthesis implanting. The reason lies in the fact that this phenomenon causes different material abrasion as compare to pure sliding or rolling alone. This study focuses on developing a knee prosthesis geometry, which fulfills the mechanical and kinematical requirements. Method: The MSC ADAMS program is used to describe the rotation of the human knee joint as a function of flexion, and to investigate how the flexion and rotation movement changes between the condyles of a multi-body model of the knee prosthesis as a function of flexion angle (in the functional arc of the knee (20-120º)). Moreover, the multi-body model with identical boundary conditions is constituted, and the numerical simulations are carried out using the MSC ADAMS program system. Results: It is concluded that the use of the multi-body model reduces time and cost since it does not need to manufacture the tibia and the femur as it requires for the knee prosthesis of the test machine. Moreover, without measuring or by dispensing with a test machine for the knee prosthesis geometry, approximation of the results of our model to a human knee is carried out directly. Conclusion: The pattern obtained by the multi-body model provides an insight for future experimental tests related to the rotation and flexion of the knee joint concerning the actual average and friction load.

Keywords: biomechanics, knee joint, rotation, flexion, kinematics, MSC ADAMS

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Authors: Yeliz Mert Kantar, İsmail Yeni̇lmez, Ibrahim Arik

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In this study, the efficiency measurement of the top fifty Turkish Universities has been conducted. The top fifty Turkish Universities are listed by The Scientific and Technological Research Council of Turkey (TÜBITAK) according to the Entrepreneur and Innovative University Index every year. The index is calculated based on four components since 2018. Four components are scientific and technological research competency, intellectual property pool, cooperation and interaction, and economic and social contribution. The four components consist of twenty-three sub-components. The 2021 list announced in January 2022 is discussed in this study. Efficiency analysis have been carried out using the Stochastic Frontier Model. Statistical significance of the sub-components that make up the index with certain weights has been examined in terms of the efficiency measurement calculated through the Stochastic Frontier Model. The relationship between the efficiency ranking estimated based on the Stochastic Frontier Model and the Entrepreneur and Innovative University Index ranking is discussed in detail.

Keywords: efficiency, entrepreneur and innovative universities, turkish universities, stochastic frontier model, tübi̇tak

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Authors: Amar Partap Singh Pharwaha, Shweta Rani

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To develop a reliable and cost effective communication platform for the telemedicine applications, novel antenna design has been presented using bacterial foraging optimization (BFO) technique. The proposed antenna geometry is achieved by etching a modified Koch curve fractal shape at the edges and a square shape slot at the center of the radiating element of a patch antenna. It has been found that the new antenna has achieved 43.79% size reduction and better resonating characteristic than the original patch. Representative results for both simulations and numerical validations are reported in order to assess the effectiveness of the developed methodology.

Keywords: BFO, electrical permittivity, fractals, Koch curve

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Authors: Oluwaseun Adeleke, Samuel Ikan, Anthony Nwala, Mopelola Raji, Fidelis Edet

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Background: The risks associated with each pregnancy is carried almost entirely by a woman; however, the decision about whether and when to get pregnant is a subject that several others contend with her to make. The self-care concept offers women of reproductive age the opportunity to take control of their health and its determinants with or without the influence of a healthcare provider, family, and friends. DMPA-SC Self-injection (SI) is becoming the cornerstone of contraceptive self-care and has the potential to expand access and create opportunities for women to take control of their reproductive health. Methodology: To obtain insight into the influences that interfere with a woman’s capacity to make contraceptive choices independently, the Delivering Innovations in Selfcare (DISC) project conducted two intensive rounds of qualitative data collection and triangulation that included provider, client, and community mobilizer interviews, facility observations, and routine program data collection. Respondents were sampled according to a convenience sampling approach and data collected analyzed using a codebook and Atlas-TI. The research team members came together for participatory analysis workshop to explore and interpret emergent themes. Findings: Insights indicate that women are increasingly finding their voice and independently seek services to prevent a deterioration of their economic situation and achieve personal ambitions. Women who hold independent decision-making power still prefer to share decision making power with their male partners. Male partners’ influence on women’s use of family planning and self-inject was most dominant. There were examples of men’s support for women’s use of contraception to prevent unintended pregnancy, as well as men withholding support. Other men outrightly deny their partners from obtaining contraceptive services and their partners cede this sexual and reproductive health right without objection. A woman’s decision to initiate family planning is affected by myths and misconceptions, many of which have cultural and religious origins. Some tribes are known for their reluctance to use contraception and often associate stigma with the pursuit of family planning (FP) services. Information given by the provider is accepted, and, in many cases, clients cede power to providers to shape their SI user journey. A provider’s influence on a client’s decision to self-inject is reinforced by their biases and concerns. Clients are inhibited by the presence of peers during group education at the health facility. Others are motivated to seek FP services by the interest expressed by peers. There is also a growing trend in the influence of social media on FP uptake, particularly Facebook fora. Conclusion: The convenience of self-administration at home is a benefit for those that contend with various forms of social influences as well as covert users. Beyond increasing choice and reducing barriers to accessing Sexual and Reproductive Health (SRH) services, it can initiate the process of self-discovery and agency in the contraceptive user journey.

Keywords: selfcare, self-empowerment, agency, DMPA-SC, contraception, family planning, influences

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Authors: B. S. Fatoyinbo, D. Stretch, O. T. Amoo, D. Allopi

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This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.

Keywords: catchment characteristics model, GIS, synthetic data, ungauged basin

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: J.Zambrano Nájera, M.Gómez Valentín

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Increases in urbanization during XX century, have brought as one major problem the increased of sediment production. Hydraulic erosion is one of the major causes of increasing of sediments in small urban catchments. Such increments in sediment yielding in header urban catchments can caused obstruction of drainage systems, making impossible to capture urban runoff, increasing runoff volumes and thus exacerbating problems of urban flooding. For these reasons, it is more and more important to study of sediment production in urban watershed for properly analyze and solve problems associated to sediments. The study of sediments production has improved with the use of mathematical modeling. For that reason, it is proposed a new physically based model applicable to small header urban watersheds that includes the advantages of distributed physically base models, but with more realistic data requirements. Additionally, in this paper the model proposed is compared with a lumped model, reviewing the results, the advantages and disadvantages between the both of them.

Keywords: erosion, hydrologic modeling, urban runoff, sediment modeling, sediment yielding, urban planning

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Authors: Lidon Lashley

Abstract:

This paper presents how the social model of disability can be used to reshape inclusive education practices in Guyana. Inclusive education in Guyana is metamorphosizing but still firmly held in the tenets of the Medical Model of Disability which influences the experiences of children with Special Education Needs and/or Disabilities (SEN/D). An ethnographic approach to data gathering was employed in this study. Qualitative data was gathered from the voices of children with and without SEN/D as well as their mainstream teachers to present the interplay of discourses and subjectivities in the situation. The data was analyzed using Adele Clarke's postmodern approach to grounded theory analysis called situational analysis. The data suggest that it is possible but will be challenging to fully contextualize and adopt Loreman's synthesis and Booths and Ainscow's Index in the two mainstream schools studied. In addition, the data paved the way for the presentation of the social model framework specific to Guyana called 'Southern Inclusive Education Framework for Guyana' and its support tool called 'The Inclusive Checker created for Southern mainstream primary classrooms.

Keywords: social model of disability, medical model of disability, subjectivities, metamorphosis, special education needs, postcolonial Guyana, inclusion, culture, mainstream primary schools, Loreman's synthesis, Booths and Ainscow's index

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Giulio Mangano, Alberto De Marco, Giovanni Zenezini

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The concept of City Logistics (CL) has emerged to improve the impacts of last mile freight distribution in urban areas. In this paper, a System Dynamics (SD) model exploring the dynamics of the diffusion of a ICT platform for CL management across different populations is proposed. For the development of the model two sources have been used. On the one hand, the major diffusion variables and feedback loops are derived from a literature review of existing diffusion models. On the other hand, the parameters are represented by the value propositions delivered by the platform as a response to some of the users’ needs. To extract the most important value propositions the Business Model Canvas approach has been used. Such approach in fact focuses on understanding how a company can create value for her target customers. These variables and parameters are thus translated into a SD diffusion model with three different populations namely municipalities, logistics service providers, and own account carriers. Results show that, the three populations under analysis fully adopt the platform within the simulation time frame, highlighting a strong demand by different stakeholders for CL projects aiming at carrying out more efficient urban logistics operations.

Keywords: city logistics, simulation, system dynamics, business model

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Authors: Moses Mwangi, Geert Verbeke, Geert Molenberghs

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Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases.

Keywords: Evaluation, Grizzle model, Jones & Kenward model, Performance measures, Simulation

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Samira Alvandi

Abstract:

In the context of the circular economy, production planning aims to eliminate waste and emissions and maximize resource efficiency. Historically production planning is challenged through arrays of uncertainty and complexity arising from the interdependence and variability of products, processes, and systems. Manufacturers worldwide are facing new challenges in tackling various environmental issues such as climate change, resource depletion, and land degradation. In managing the inherited complexity and uncertainty and yet maintaining profitability, the manufacturing sector is in need of a holistic framework that supports energy efficiency and carbon emission reduction schemes. The proposed framework addresses the current challenges and integrates simulation modeling with optimization for finding optimal machine-job allocation to maximize throughput and total energy consumption while minimizing lead time. The aluminium refinery facility in western Sydney, Australia, is used as an exemplar to validate the proposed framework.

Keywords: smart production planning, simulation-optimisation, energy aware capacity planning, energy intensive industries

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Authors: Pavel Damborsky, Martina Zamorova, Jaroslav Katrlik

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Prostate cancer is annually the most common newly diagnosed cancer among men. An extensive number of evidence suggests that traditional serum Prostate-specific antigen (PSA) assay still suffers from a lack of sufficient specificity and sensitivity resulting in vast over-diagnosis and overtreatment. Thus, the early-stage detection of prostate cancer (PCa) plays undisputedly a critical role for successful treatment and improved quality of life. Over the last decade, particular altered glycans have been described that are associated with a range of chronic diseases, including cancer and inflammation. These glycans differences enable a distinction to be made between physiological and pathological state and suggest a valuable biosensing tool for diagnosis and follow-up purposes. Aberrant glycosylation is one of the major characteristics of disease progression. Consequently, the aim of this study was to develop a more reliable tool for early-stage PCa diagnosis employing lectins as glyco-recognition elements. Biosensor and biochip technology putting to use lectin-based glyco-profiling is one of the most promising strategies aimed at providing fast and efficient analysis of glycoproteins. The proof-of-concept experiments based on sandwich assay employing anti-PSA antibody and an aptamer as a capture molecules followed by lectin glycoprofiling were performed. We present a lectin-based biosensing assay for glycoprofiling of serum biomarker PSA using different biosensor and biochip platforms such as label-free surface plasmon resonance (SPR) and microarray with fluorescent label. The results suggest significant differences in interaction of particular lectins with PSA. The antibody-based assay is frequently associated with the sensitivity, reproducibility, and cross-reactivity issues. Aptamers provide remarkable advantages over antibodies due to the nucleic acid origin, stability and no glycosylation. All these data are further step for construction of highly selective, sensitive and reliable sensors for early-stage diagnosis. The experimental set-up also holds promise for the development of comparable assays with other glycosylated disease biomarkers.

Keywords: biomarker, glycosylation, lectin, prostate cancer

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Authors: Dora Vajda, Somayyeh Mohammadi, Sandor Rozsa

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Predicting the relationship satisfaction based on the personality characteristics of both partners has a long history. The association between relationship quality and personality traits has been previously demonstrated. Personality traits are most commonly assessed using the Five-Factor Model. The present study has focused on Cloninger's psychobiological model of personality that accounts for dimensions of both temperament and character. The goal of this study was to examine the actor and partner effect of couple's personality on relationship outcomes. In total, 184 heterosexual couples completed the Temperament and Character Inventory (TCI) and the Dyadic Adjustment Scale. The analysis was based on Actor-Partner Interdependence Model (APIM) using multilevel modeling (MLwiN). Results showed that character dimensions Self-Directedness and Cooperativeness had a statistically meaningful actor and partner effect on both partner's relationship quality. However, male's personality temperament dimension Reward Dependence had an only actor effect on his relationship quality. The findings contribute to the literature by highlighting the role of character dimensions of personality in romantic relationships.

Keywords: APIM (actor-partner interdependence model), MLwiN, personality, relationship quality

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Authors: Hussam Ashwi, Magbool Oraiby, Ali Muyidi, Hamad Al-Oufi, Mohammed Al-Oufi, Adel Al-Juhani, Salman Al-Zemaa, Saeed Al-Shahrani, Amal Abuallah, Wedad Sherwani, Mohammed Alattas, Ibraheem Attafi

Abstract:

Ethanol must be accurately identified and quantified to establish their use and contribution in criminal cases and forensic medicine. In some situations, it may be necessary to reanalyze an old specimen; therefore, it is essential to comprehend the effect of storage conditions and how long the result of a reanalyzed specimen can be reliable and reproducible. Additionally, ethanol can be produced via multiple in vivo and in vitro processes, particularly in diabetic patients, and the results can be affected by storage conditions and time. In order to distinguish between in vivo and in vitro alcohol generation in diabetes patient urine samples, various factors should be considered. This study identifies and quantifies ethanol and EtG in diabetic patients' urine samples stored in two different settings over time. Ethanol levels were determined using gas chromatography-headspace (GC-HS), and ethyl glucuronide (EtG) levels were determined using the immunoassay (RANDOX) technique. Ten urine specimens were collected and placed in a standard container. Each specimen was separated into two containers. The specimens were divided into two groups: those kept at room temperature (25 °C) and those kept cold (2-8 °C). Ethanol and EtG levels were determined serially over a two-week period. Initial results showed that none of the specimens tested positive for ethanol or EtG. At room temperature (15-25 °C), 7 and 14 days after the sample was taken, the average concentration of ethanol increased from 1.7 mg/dL to 2 mg/dL, and the average concentration of EtG increased from 108 ng/mL to 186 ng/mL. At 2–8 °C, the average ethanol concentration was 0.4 and 0.5 mg/dL, and the average EtG concentration was 138 and 124 ng/mL seven and fourteen days after the sample was collected, respectively. When ethanol and EtG levels were determined 14 days post collection, they were considerably lower than when stored at room temperature. A considerable increase in EtG concentrations (14-day range 0–186 ng/mL) is produced during room-temperature storage, although negative initial results for all specimens. Because EtG might be produced after a sampling collection, it is not a reliable indicator of recent alcohol consumption. Given the possibility of misleading EtG results due to in vitro EtG production in the urine of diabetic patients.

Keywords: ethyl glucuronide, ethanol, forensic toxicology, diabetic

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Authors: Mojtaba Farzaneh, Majid Baghaei Nejad

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This article proposes a new Flexible Capacitive Tactile Sensors based on paper sheets. This method combines the parameters of sensor's material and dielectric, and forms a new model of flexible capacitive sensors. The present article tries to present a practical explanation of this method's application and advantages. With the use of this new method, it is possible to make a more flexibility and accurate sensor in comparison with the current models. To assess the performance of this model, the common capacitive sensor is simulated and the proposed model of this article and one of the existing models are assessed. The results of this article indicate that the proposed model of this article can enhance the speed and accuracy of tactile sensor and has less error in comparison with the current models. Based on the results of this study, it can be claimed that in comparison with the current models, the proposed model of this article is capable of representing more flexibility and more accurate output parameters for touching the sensor, especially in abnormal situations and uneven surfaces, and increases accuracy and practicality.

Keywords: capacitive sensor, paper sheets, flexible, tactile, uneven

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Authors: Bilgehan Gültekin, Tuba Gültekin

Abstract:

Peace diplomacy is the most important international tool to keep peace all over the world. The study titled “peace diplomacy for international relations” is consist of three part. In the first part, peace diplomacy is going to be introduced as a tool of peace communication and peace management. And, in this part, peace communication will be explained by international communication perspective. In the second part of the study,public relations events and communication campaigns will be developed originally for peace diplomacy. In this part, it is aimed original public communication dialogue management tools for peace diplomacy. the aim of the final part of the study, is to produce original public communication model for international relations. The model includes peace modules, peace management projects, original dialogue procedures and protocols, dialogue education, dialogue management strategies, peace actors, communication models, peace team management and public diplomacy steps. The creative part of the study aims to develop a model used for international relations for all countries. Creative Peace Diplomacy Model will be developed in the case of Turkey-Turkey-France and Turkey-Greece relations. So, communication and public relations events and campaigns are going to be developed as original for only this study.

Keywords: peace diplomacy, public communication model, dialogue management, international relations

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Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan

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With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.

Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization

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Authors: Tiffany Gallagher, Sheila Bennett

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Based on data from a larger study, this research is based in a small school district in Ontario, Canada, that has made a transition from self-contained classes for students with exceptionalities to inclusive classroom placements for all students with their age-appropriate peers. The school board aided this transition by hiring Inclusion Coaches with a background in special education to work alongside teachers as partners and inform their inclusive practice. Based on qualitative data from four focus groups conducted with Inclusion Coaches, as well as four blog-style reflections collected at various points over two years, six principles of inclusive practice were identified for coaches. The six principles form a model during transition: pre-requisite, process, precipice, promotion, proof, and promise. These principles are encapsulated in a visual model of a spiraling staircase displaying the conditions that exist prior to coaching, during coaching interactions and considerations for the sustainability of coaching. These six principles are re-iterative and should be re-visited each time a coaching interaction is initiated. Exploring inclusion coaching as a model emulates coaching in other contexts and allows us to examine an established process through a new lens. This research becomes increasingly important as more school boards transition toward inclusive classrooms, The Six ‘P’ Model: Principles of Inclusive Practice for Inclusion Coaches allows for a unique look into a scaffolding model of building educator capacity in an inclusive setting.

Keywords: capacity building, coaching, inclusion, special education

Procedia PDF Downloads 240