Search results for: irregular network

Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

Procedia PDF Downloads 669

Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Thulane Paepae, Tumisang Seodigeng

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This paper demonstrates the use of a method of synthesizing process flowsheets using a graphical tool called the GH-plot and in particular, to look at how it can be used to compare the reactions of a combined simultaneous process with regard to their thermodynamics. The technique uses fundamental thermodynamic principles to allow the mass, energy and work balances locate the attainable region for chemical processes in a reactor. This provides guidance on what design decisions would be best suited to developing new processes that are more effective and make lower demands on raw material and energy usage.

Keywords: attainable regions, dimethyl ether, optimal reaction network, GH Space

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Authors: Lawrence Williams

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As the mechanism which converts domains to internet protocol (IP) addresses, Domain Name System (DNS) is an essential part of internet usage. It was not designed securely and can be subject to attacks. DNS attacks have become more frequent and sophisticated and the need for detecting and preventing them becomes more important for the modern network. DNS tunnelling attacks are one type of attack that are primarily used for distributed denial-of-service (DDoS) attacks and data exfiltration. Discussion of different techniques to detect and prevent DNS tunneling attacks is done. The methods, models, experiments, and data for each technique are discussed. A proposal about feasibility is made. Future research on these topics is proposed.

Keywords: DNS, tunneling, exfiltration, botnet

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Authors: Asim Abbasi, Muhammad Sufyan, Iqra, Muhammad Ibrahim Shahid, Muhammad Ashfaq

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The foundation of human life on earth relies on many ecosystem services provided by insects of which pollination owes a vital role. The pollination service offered by insects has annual worth of approximately €153 billion. The majority of the flowering plants depends on entomophiles pollination for their reproduction and formation of seeds and fruits. The quantity and quality of insect pollination have multiple implications for stable ecosystem, diverse species level, food security and climate change resilience. The rapidly mounting human population, depletion of natural resources and the global climate change forced us to enter an era of pollination crisis. Climate change not only alters the phenology, population abundance and geographic ranges of different pollinators but also hinders their pollination activities. The successful pollination process relies heavily on the synchronization of biological events of pollinators with the phenological stages of the flowering plants. However, there are possibilities that impending climatic changes may result in asynchrony between plant-pollinators interactions and also mitigate the extent of pollination. The trophic mismatch mostly occurs when pollinators and plants inhabiting the same environment use different environmental cues to regulate their biological events, as these cues are not equally affected by climate change. Synchrony has also been disrupted when one of the interacting species has migratory nature and depend on cues for migration. Moreover, irregular rainfalls and up-surging temperature also disrupts the foraging behaviour of pollinators resulting in reduced flowers visits by insect. Climate change has a direct impact on the behavior and physiology of honey bees, the best known pollinators owing to their extreme floral fidelity. Rising temperature not only alleviates the quantity and quality of floral environment but also alters the bee’s colony harvesting and development ability. Furthermore, a possible earlier decline of flowers is expected in a growing season due to this rising temperature. This may also lead to disrupt the efficiency bumblebee queen that require a constant and adequate nectar and pollen supply throughout the entire growing season for healthy colony production. Considering the role of insect pollination in our ecosystem, their associated risks regarding climate change should be addressed properly for devising a well-focused research needed for their conservation.

Keywords: climate change, phenological, pollination, synchronization

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Authors: Ankit Shah, R. K. Shrivastava

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Flood and drought are two main features of hydrology which affect the human life. Floods are natural disasters which cause millions of rupees’ worth of damage each year in India and the whole world. Flood causes destruction in form of life and property. An accurate estimate of the flood damage potential is a key element to an effective, nationwide flood damage abatement program. Also, the increase in demand of water due to increase in population, industrial and agricultural growth, has let us know that though being a renewable resource it cannot be taken for granted. We have to optimize the use of water according to circumstances and conditions and need to harness it which can be done by construction of hydraulic structures. For their safe and proper functioning of hydraulic structures, we need to predict the flood magnitude and its impact. Hydraulic structures play a key role in harnessing and optimization of flood water which in turn results in safe and maximum use of water available. Mainly hydraulic structures are constructed on ungauged sites. There are two methods by which we can estimate flood viz. generation of Unit Hydrographs and Flood Frequency Analysis. In this study, Regional Flood Frequency Analysis has been employed. There are many methods for estimating the ‘Regional Flood Frequency Analysis’ viz. Index Flood Method. National Environmental and Research Council (NERC Methods), Multiple Regression Method, etc. However, none of the methods can be considered universal for every situation and location. The Narmada basin is located in Central India. It is drained by most of the tributaries, most of which are ungauged. Therefore it is very difficult to estimate flood on these tributaries and in the main river. As mentioned above Artificial Neural Network (ANN)s and Multiple Regression Method is used for determination of Regional flood Frequency. The annual peak flood data of 20 sites gauging sites of Narmada Basin is used in the present study to determine the Regional Flood relationships. Homogeneity of the considered sites is determined by using the Index Flood Method. Flood relationships obtained by both the methods are compared with each other, and it is found that ANN is more reliable than Multiple Regression Method for the present study area.

Keywords: artificial neural network, index flood method, multi layer perceptrons, multiple regression, Narmada basin, regional flood frequency

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Authors: Akbarova Gulnozakhon

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Relevance. Physical exercises can prolong the function of the growth zones of long tubular bones, delay the fusion of the epiphyses and diaphyses of bones and, thus, increase the growth of the body. At the same time, intensive strength exercises can accelerate the process of ossification of bone growth zones and slow down their growth in length. The influence of physical exercises on the process of biological maturation is noted. Gymnastics, which requires intense speed and strength loads, delays puberty. On the other hand, it is indicated that the relatively slow puberty of gymnasts is associated with the selection of girls with a special somatotype in this sport. It was found that the later onset of menstruation in female athletes does not have a negative effect on the maturation process and fertility (the ability to procreate). Observations are made about the normalizing influence of sports on the puberty of girls. The purpose of the study. Our goal is to study physical activity of varying intensity on the formation of secondary sexual characteristics and hormonal status of girls in adolescence. Each biological process peculiar to a given organism is not in a stationary state, but fluctuates with a certain frequency. According to the duration, there are, for example, circadian cycles, and infradian cycles, a typical example of which is the menstrual cycle. Materials and methods, results. Violations of menstrual function in athletes were detected by applying a questionnaire survey that contains several paragraphs and sub-paragraphs where passport data, anthropometric indicators, taking into account anthropometric indices, information about the menstrual cycle are indicated. Of 135 female athletes aged 1-3 to 16 years engaged in various sports - gymnasts, menstrual function disorders were noted in 86.7% (primary or secondary amenorrhea, irregular MC), in swimming-in 57.1%. The general condition also changes during the menstrual cycle. In a large percentage of cases, athletes indicate an increase in irritability in the premenstrual (45%) and menstrual (36%) phases. During these phases, girls note an increase in fatigue of 46.5% and 58% (respectively). In girls, secondary sexual characteristics continue to form during puberty and the clearest indicator of the onset of puberty is the age of the onset of the first menstruation - menarche. Conclusions. 1. Physical exercise has a positive effect on all major systems of the body and thus promotes health.2. Along with a beneficial effect on human health, physical exercise, if the requirements of sports are not observed, can be harmful.

Keywords: girls health, anthropometric, physical development, reproductive health

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Authors: Eze Chinwe Catherine, Orji Nkiru Marykate, Anyaegbunam L. C., Igbodika M.C.

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Uterine Leiomyomata (ULs) are the most frequently occurring pelvic and gynaecologic tumors in premenopausal women, occurring globally with a prevalence of 21.4%. UL represents a major public health problem in African women; therefore, this study aimed to reveal public health impact and risk factors associated with uterine leiomyomata among women in Imo state. A convenience sample of 2965 women was studied for gynaecological cases from October 2020 to March 2021 at the selected clinics of study. Eligible women were recruited to participate in a non interventional descriptive cross-sectional study. Data on socio demographic and gynaecological characteristics, BMI, parity, age, age at menarche, knowledge, attitudes, and perception were collected using a structured questionnaire, guided interviews, anthropometrics, and haematological tests. These were analyzed using SPSS Version 23. Associations between continuous variables were analysed appropriately and tested at 95% confidence level and standard error of 5%. A total of 652(22.0%) were diagnosed having uterine Leiomyomata (UL), and the overall prevalence of UF at clinics/Diagnostic centre in Imo State was 22%. A total of 652 women (46.1%) responded. More than half of the women had a parity of zero (1623: 54.8%), 664 (22.4%) had a parity of 1-2, and 491 (16.6%) had a parity of 3-4. Majority (68.6%) indicated that they experience an irregular menstrual cycle, and a similar proportion (67%) number experience pelvic pain. Age was found as a significant associating factor of uterine fibroids in this study (p=0.046, χ2= 6.158), lowest among the women between 16 to 25 years old and highest among the women between 36 – 45 years of age. The rate of UF was found to be 62.1% on the studied women menarche age of 11 years old or less while it was approximately 18% among the women whose age at menarche were at least 14 years old. Education ((p=0.003, χ²= 13.826), residency (p=0.066, χ²= 3.372). BMI (p= 0.000, χ²=102.36) were significantly associated with the risk of UL. Some of the Clinical presentation includes anaemia, abdominal pelvic mass, and infertility. The poor positive perception was obtained on the general perception (16.7%) as well as on treatment seeking behavior (28%). The study concluded that UL had a significant impact on health related quality of life on respondents due to its relatively high prevalence and their probable impact on patient’s quality of life.UL was especially prevalent in women aged between 36 to 45 years, nulliparous women, and women of higher BMI. Community enlightenment to enhance knowledge, attitude, and perception on fibroids and risk factors necessary to ensure early diagnosis and presentation, including patient centered treatment option.

Keywords: fibroids, prevalence, risk factors, body mass index, menarche, anaemia, KAP

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Authors: Atitallah Sofien, Bouyahia Olfa, Romdhani Meriam, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

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Introduction: Spondylothoracic dysostosis, also known as Jarcho-Levin syndrome, is defined by a shortened neck and thorax, a protruding abdomen, inguinal and umbilical hernias, atypical spinal structure and rib fusion, leading to restricted chest movement or difficulty in breathing, along with urinary tract abnormalities and, potentially severe scoliosis. Aim: This is the case of a patient diagnosed with Jarcho-Levin syndrome, aiming to detail the range of abnormalities observed in this syndrome, the observed complications, and the therapeutic approaches employed. Results: A three-month-old male infant, born of a consanguineous marriage, delivered at full term by cesarean section, was admitted to the pediatric department for severe acute bronchiolitis. In his prenatal history, morphological ultrasound revealed macrosomia, a shortened spine, irregular vertebrae with thickened skin, normal fetal cardiac ultrasound, and the absence of the right kidney. His perinatal history included respiratory distress, requiring ventilatory support for five days. Upon physical examination, he had stunted growth, scoliosis, a short neck and trunk, longer upper limbs compared to lower limbs, varus equinus in the right foot, a neural tube defect, a low hairline, and low-set ears. Spondylothoracic dysostosis was suspected, leading to further investigations, including a normal transfontaneous ultrasound, a spinal cord ultrasound revealing a lipomyelocele-type closed dysraphism with a low-attached cord, an abdominal ultrasound indicating a single left kidney, and a cardiac ultrasound identifying Kommerell syndrome. Due to a lack of resources, genetic testing could not be performed, and the diagnosis was based on clinical criteria. Conclusion: Jarcho-Levin syndrome can result in a mortality rate of about 50%, primarily due to respiratory complications associated with thoracic insufficiency syndrome. Other complications, like heart and neural tube defects, can also lead to premature mortality. Therefore, early diagnosis and comprehensive treatment involving various specialists are essential.

Keywords: Jarcho-Levin syndrome, congenital disorder, scoliosis, spondylothoracic dysostosis, neural tube defect

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is the ability to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle is vital in automotive engineering. Stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswind and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle especially with the worrying increase of vehicle collision every day. With better safety performance on a vehicle, every driver will be more confidence driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in four-wheel vehicle especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on performance of both suspension systems.

Keywords: automobile, camber thrust, cornering force, suspension

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Authors: Beatriz Cortés, Andrés Boza, David Pérez, Llanos Cuenca

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The Internet of Things (IoT) field is been applied in industries with different purposes. Sensing Enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the internet. These fields have come into focus recently on the enterprises and there is some evidence of the use and implications in supply chain management while finding it as an interesting aspect to work on. This paper presents a revision and proposals of IoT applications in supply chain management.

Keywords: industrial, internet of things, production systems, sensing enterprises, sensor, supply chain management

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Authors: Janine Just, Kerstin Meints

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The research aims to compare vocabulary size and grammatical development between US American English- and British English-speaking children at age 2;0. As there is evidence that precocious children with large vocabularies develop grammar skills earlier than their typically developing peers, it was investigated if this also holds true across varieties of English. Thus, if US American children start to produce words earlier than their British counterparts, this could mean that US children are also at an advantage in the early developmental stages of acquiring grammar. This research employs a British English adaptation of the MacArthur-Bates CDI Words and Sentences (Lincoln Toddler CDI) to compare vocabulary and also grammar scores with the updated US Toddler CDI norms. At first, the Lincoln TCDI was assessed for its concurrent validity with the Preschool Language Scale (PLS-5 UK). This showed high correlations for the vocabulary and grammar subscales between the tests. In addition, the frequency of the Toddler CDI’s words was also compared using American and British English corpora of adult spoken and written language. A paired-samples t-test found a significant difference in word frequency between the British and the American CDI demonstrating that the TCDI’s words were indeed of higher frequency in British English. We then compared language and grammar scores between US (N = 135) and British children (N = 96). A two-way between groups ANOVA examined if the two samples differed in terms of SES (i.e. maternal education) by investigating the impact of SES and country on vocabulary and sentence complexity. The two samples did not differ in terms of maternal education as the interaction effects between SES and country were not significant. In most cases, scores were not significantly different between US and British children, for example, for overall word production and most grammatical subscales (i.e. use of words, over- regularizations, complex sentences, word combinations). However, in-depth analysis showed that US children were significantly better than British children at using some noun categories (i.e. people, objects, places) and several categories marking early grammatical development (i.e. pronouns, prepositions, quantifiers, helping words). However, the effect sizes were small. Significant differences for grammar were found for irregular word forms and progressive tense suffixes. US children were more advanced in their use of these grammatical categories, but the effect sizes were small. In sum, while differences exist in terms of vocabulary and grammar ability, favouring US children, effect sizes were small. It can be concluded that most British children are ‘catching up’ with their US American peers at age 2;0. Implications of this research will be discussed.

Keywords: first language acquisition, grammar, parent report instrument, vocabulary

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Authors: Santos Andressa M. N.

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The research brings reflections on the importance of Instagram for the clothing trade, aiming to analyze the use of this social network as a sales tool by small companies in the fashion/clothing sector in Boqueirão-PI. Thus, field research was carried out, with the application of questionnaires, to raise and analyze data related to the topic. Thus, it is believed that Instagram positively influences the dissemination, visibility, reach and profitability of companies in Boqueirão do Piauí. The survey had a low number of companies due to the lack of availability of the owners during the COVID-19 pandemic.

Keywords: Instagram, sales, fashion, marketing

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Authors: Kurt Willems

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The right to education is a fundamental human right, which has been entrenched in many international and regional treaties and national constitutions. Nevertheless, practice shows that many obstacles impede easy access to quality education for refugees. Overall, the material effects of international human rights legislation on improving (irregular) migrants’ access to social rights in the European countries have remained limited due to the lack of guarantees on effective incorporation in the municipal legal order and due to the lack of effective enforcement mechanisms. After the recent refugee crisis in Europe, this issue has grown in importance. The presentation aims to give a brief overview of the most important issues impeding the effective enforceability of the right to education for refugees. I. Do refugees fall within the scope of application of the relevant human rights treaties and to which extent can they invoke human rights treaties in domestic courts to set aside domestic legislation? II. How is the justiciability of the right to education organized in those treaties? III. What is the legal answer to questions raised in practice when dealing with the influx of refugees in Europe: (i) can refugees be placed in separate schools or classes until they can follow the regular curriculum?; (ii) can higher school fees be asked from pupils without legal documents?; (iii) do refugees have a right to be taught in their own native language until they learn to speak the national language? To answer the above questions, the doctrinal and comparative legal method will be used. The normative framework, as interpreted within Europe, will be distilled from the recent and relevant international treaties and European law instruments (in particular the Convention on the Rights of the Child, the European Convention on human rights, the European Social Charter and the International Covenant on Economic, Social and Cultural Rights) and their underlying policy documents, the legal literature, the (limited) European jurisprudence, and the general comments to those treaties. The article is mainly descriptive in nature. Its aim is to serve as a summary of the legal provisions, case law and legal literature on the topic of the right to education for refugees. The research shows that the reasons for the delicate enforceability of the rights to and the rights in education are multifold. The research will categorize the different contributing factors under the following headings: (i) problems related to the justiciability of international law as such; (ii) problems specifically related to the educational field; (iii) problems related to policy issues in the refugee debate. By categorizing the reasons contributing to the difficult enforceability of the right to education and the rights in education for refugees, this research hopes to facilitate the search for solutions to this delicate problem.

Keywords: right to education, refugees, discrimination, enforceability of human rights

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Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

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Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: desalting unit, crude oil, neural networks, simulation, recovery, separation

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Authors: Linda Smail, Zineb Azouz

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Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks.

Keywords: Bayesian networks, D-Separation, level two Bayesian networks, factorization of computation

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Authors: M. P. S. Tomar, Himanshu R. Joshi, P. Jagapathi Ramayya, Rakhi Vaish, A. B. Shrivastav

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The morphology of an organ system varies according to the feeding habit, habitat and nature of their life-style. This phenomenon is called adaptation. During evolution these morphological changes make the system species specific so the study on the differential characteristics of them makes the understanding regarding the morpho-physiological adaptation easier. Hence the present study was conducted on esophagus of pariah kite, median egret, goshawk, dove and duck. Esophagus in all birds was comprised of four layers viz. Tunica mucosa, Tunica submucosa, Tunica muscularis and Tunica adventitia. The mucosa of esophagus showed longitudinal folds thus the lumen was irregular. The epithelium was stratified squamous in all birds but in Median egret the cells were large and vacuolated. Among these species very thick epithelium was observed in goshawk and duck but keratinization was highest in dove. The stratum spongiosum was 7-8 layers thick in both Pariah kite and Goshawk. In all birds, the glands were alveolar mucous secreting type. In Median egret and Pariah kite, these were round or oval in shape and with or without lumen depending upon the functional status whereas in Goshawk the shape of the glands varied from spherical / oval to triangular with openings towards the lumen according to the functional activity and in dove these glands were oval in shape. The glands were numerous in number in egret while one or two in each fold in goshawk and less numerous in other three species. The core of the mucosal folds was occupied by the lamina propria and showed large number of collagen fibers and cellular infiltration in pariah kite, egret and dove where as in goshawk and duck, collagen and reticular fibers were fewer and cellular infiltration was lesser. Lamina muscularis was very thick in all species and it was comprised of longitudinally arranged smooth muscle fibers. In Median egret, it was in wavy pattern. Tunica submucosa was very thin in all species. Tunica muscularis was mostly comprised of circular smooth muscle bundles in all species but the longitudinal bundles were very few in number and not continuous. The tunica adventitia was comprised of loose connective tissue fibers containing collagen and elastic fibers with numerous small blood vessels in all species. Further, it was observed that the structure of esophagus in birds varies according to their feeding habits.

Keywords: dove, duck, egret, esophagus, goshawk, kite

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Carlos Teodoro, Oscar Bautista

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In this work, we analyze theoretically the mass transport in a time-periodic electroosmotic flow through a parallel flat plate microchannel under different periodic functions of the applied external electric field. The microchannel connects two reservoirs having different constant concentrations of an electro-neutral solute, and the zeta potential of the microchannel walls are assumed to be uniform. The governing equations that allow determining the mass transport in the microchannel are given by the Poisson-Boltzmann equation, the modified Navier-Stokes equations, where the Debye-Hückel approximation is considered (the zeta potential is less than 25 mV), and the species conservation. These equations are nondimensionalized and four dimensionless parameters appear which control the mass transport phenomenon. In this sense, these parameters are an angular Reynolds, the Schmidt and the Péclet numbers, and an electrokinetic parameter representing the ratio of the half-height of the microchannel to the Debye length. To solve the mathematical model, first, the electric potential is determined from the Poisson-Boltzmann equation, which allows determining the electric force for various periodic functions of the external electric field expressed as Fourier series. In particular, three different excitation wave forms of the external electric field are assumed, a) sawteeth, b) step, and c) a periodic irregular functions. The periodic electric forces are substituted in the modified Navier-Stokes equations, and the hydrodynamic field is derived for each case of the electric force. From the obtained velocity fields, the species conservation equation is solved and the concentration fields are found. Numerical calculations were done by considering several binary systems where two dilute species are transported in the presence of a carrier. It is observed that there are different angular frequencies of the imposed external electric signal where the total mass transport of each species is the same, independently of the molecular diffusion coefficient. These frequencies are called crossover frequencies and are obtained graphically at the intersection when the total mass transport is plotted against the imposed frequency. The crossover frequencies are different depending on the Schmidt number, the electrokinetic parameter, the angular Reynolds number, and on the type of signal of the external electric field. It is demonstrated that the mass transport through the microchannel is strongly dependent on the modulation frequency of the applied particular alternating electric field. Possible extensions of the analysis to more complicated pulsation profiles are also outlined.

Keywords: electroosmotic flow, mass transport, oscillatory flow, species separation

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Authors: R. Loukil, M. Chtourou, T. Damak

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This paper investigates the training of multilayer neural network using the classic approach. Then, for estimation purposes, we suggest the use of a specific neural observer that we study its training algorithm which is the back-propagation one in the case of the disponibility of the state and in the case of an unmeasurable state. A MATLAB simulation example will be studied to highlight the usefulness of this kind of observer.

Keywords: training, estimation purposes, neural observer, back-propagation, unmeasurable state

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Authors: Desmond Young

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In building services electrical design, a key element of any project will be assessing the electrical load requirements. This needs to be done early in the design process to allow the selection of infrastructure that would be required to meet the electrical needs of the type of building. The type of building will define the type of assessment made, and the values applied in defining the maximum demand for the building, and ultimately the size of supply or infrastructure required, and the application that needs to be made to the distribution network operator, or alternatively to an independent network operator. The fact that this assessment needs to be undertaken early in the design process provides limits on the type of assessment that can be used, as different methods require different types of information, and sometimes this information is not available until the latter stages of a project. A common method applied in the earlier design stages of a project, typically during stages 1,2 & 3, is the use of benchmarks. It is a possibility that some of the benchmarks applied are excessive in relation to the current loads that exist in a modern installation. This lack of accuracy is based on information which does not correspond to the actual equipment loads that are used. This includes lighting and small power loads, where the use of more efficient equipment and lighting has reduced the maximum demand required. The electrical load can be used as part of the process to assess the heat generated from the equipment, with the heat gains from other sources, this feeds into the sizing of the infrastructure required to cool the building. Any overestimation of the loads would contribute to the increase in the design load for the heating and ventilation systems. Finally, with the new policies driving the industry to decarbonise buildings, a prime example being the recently introduced London Plan, loads are potentially going to increase. In addition, with the advent of the pandemic and changes to working practices, and the adoption of electric heating and vehicles, a better understanding of the loads that should be applied will aid in ensuring that infrastructure is not oversized, as a cost to the client, or undersized to the detriment of the building. In addition, more accurate benchmarks and methods will allow assessments to be made for the incorporation of energy storage and renewable technologies as these technologies become more common in buildings new or refurbished.

Keywords: energy, ADMD, electrical load assessment, energy benchmarks

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: R. M. López-Gutiérrez, L. Cardoza-Avendaño, H. Cervantes-de Ávila, J. A. Michel-Macarty, C. Cruz-Hernández, A. Arellano-Delgado, R. Carmona-Rodríguez

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In this paper, synchronization of multiple chaotic semiconductor lasers is achieved by appealing to complex system theory. In particular, we consider dynamical networks composed by semiconductor laser, as interconnected nodes, where the interaction in the networks are defined by coupling the first state of each node. An interesting case is synchronized with master-slave configuration in star topology. Nodes of these networks are modeled for the laser and simulated by Matlab. These results are applicable to private communication.

Keywords: chaotic laser, network, star topology, synchronization

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Authors: Sonia Boscenco, Zihan Wang, Euclides José de Mendoça Filho, João Paulo Hoppe, Irina Pokhvisneva, Geoffrey B.C. Hall, Michael J. Meaney, Patricia Pelufo Silveira

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Entropy can be described as a measure of the number of states of a system, and when used in the context of physiological time-based signals, it serves as a measure of complexity. In functional connectivity data, entropy can account for the moment-to-moment variability that is neglected in traditional functional magnetic resonance imaging (fMRI) analyses. While brain fMRI resting state entropy has been associated with some pathological conditions like schizophrenia, no investigations have explored the association between brain entropy measures and individual differences in child behavior in healthy children. We describe a novel exploratory approach to evaluate brain fMRI resting state data in two child cohorts, and MAVAN (N=54, 4.5 years, 48% males) and GUSTO (N = 206, 4.5 years, 48% males) and its associations to child behavior, that can be used in future research in the context of child exposures and long-term health. Following rs-fMRI data pre-processing and Shannon entropy calculation across 32 network regions of interest to acquire 496 unique functional connections, partial correlation coefficient analysis adjusted for sex was performed to identify associations between entropy data and Strengths and Difficulties questionnaire in MAVAN and Child Behavior Checklist domains in GUSTO. Significance was set at p < 0.01, and we found eight significant associations in GUSTO. Negative associations were found between two frontoparietal regions and cerebellar posterior and oppositional defiant problems, (r = -0.212, p = 0.006) and (r = -0.200, p = 0.009). Positive associations were identified between somatic complaints and four default mode connections: salience insula (r = 0.202, p < 0.01), dorsal attention intraparietal sulcus (r = 0.231, p = 0.003), language inferior frontal gyrus (r = 0.207, p = 0.008) and language posterior superior temporal gyrus (r = 0.210, p = 0.008). Positive associations were also found between insula and frontoparietal connection and attention deficit / hyperactivity problems (r = 0.200, p < 0.01), and insula – default mode connection and pervasive developmental problems (r = 0.210, p = 0.007). In MAVAN, ten significant associations were identified. Two positive associations were found = with prosocial scores: the salience prefrontal cortex and dorsal attention connection (r = 0.474, p = 0.005) and the salience supramarginal gyrus and dorsal attention intraparietal sulcus (r = 0.447, p = 0.008). The insula and prefrontal connection were negatively associated with peer problems (r = -0.437, p < 0.01). Conduct problems were negatively associated with six separate connections, the left salience insula and right salience insula (r = -0.449, p = 0.008), left salience insula and right salience supramarginal gyrus (r = -0.512, p = 0.002), the default mode and visual network (r = -0.444, p = 0.009), dorsal attention and language network (r = -0.490, p = 0.003), and default mode and posterior parietal cortex (r = -0.546, p = 0.001). Entropy measures of resting state functional connectivity can be used to identify individual differences in brain function that are correlated with variation in behavioral problems in healthy children. Further studies applying this marker into the context of environmental exposures are warranted.

Keywords: child behaviour, functional connectivity, imaging, Shannon entropy

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Authors: Marina Vicario, Amadeo Argüelles, Pilar Gómez, Carlos Hernández

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In this paper, the Computer Research Network of the National Polytechnic Institute of Mexico proposes a paradigmatic model for the inference of cognitive patterns in digital learning systems. This model leads to metadata architecture useful for analysis and prediction in online learning systems; especially on MOOc's architectures. The model is in the design phase and expects to be tested through an institutional of courses project which is going to develop for the MOOc.

Keywords: cognitive footprints, learning analytics, predictive learning, digital learning, educational computing, educational informatics

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Authors: Wen-Bin Chiou, Hsiao-Chiao Weng

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Social networking sites (SNS) such as Facebook and Twitter are low-cost tools that can promote the creation of social connections by providing a convenient platform that can be accessed at any time. In the current research, a laboratory experiment was conducted test the hypothesis that reminders of losing SNS would alter the impact of social events, especially those involving social exclusion. Specifically, this study explored whether losing SNS would intensify perceived social distress induced by exclusionary bogus feedback. Eighty-eight Facebook users (46 females, 42 males; mean age = 22.6 years, SD = 3.1 years) were recruited via campus posters and flyers at a national university in southern Taiwan. After participants provided consent, they were randomly assigned to a 2 (SNS non-use vs. neutral) between-subjects experiment. Participants completed an ostensible survey about online social networking in which we included an item about the time spent on SNS per day. The last question was used to manipulate thoughts about losing SNS access. Participants under the non-use condition were asked to record three conditions that would render them unable to use SNS (e.g., a network adaptor problem, malfunctioning cable modem, or problems with Internet service providers); participants under the neutral condition recorded three conditions that would render them unable to log onto the college website (e.g., server maintenance, local network or firewall problems). Later, this experiment employed a bogus-feedback paradigm to induce social exclusion. Participants then rated their social distress on a four-item scale, identical to that of Experiment 1 (α = .84). The results showed that thoughts of losing SNS intensified distress caused by social exclusion, suggesting that the loss of SNS has a similar effect to the loss of a primary source for social reconnections. Moreover, the priming effects of SNS on perceived distress were more prominent for heavy users. The demonstrated link between the idea of losing SNS use and increased pain of social exclusion manifests the importance of SNS as a crucial gateway for acquiring and rebuilding social connections. Use of online social networking appears to be a two-edged sword for coping with social exclusion in human lives in the e-society.

Keywords: online social networking, perceived distress, social exclusion, SNS

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Authors: Reena Aggarwal

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The use of textile chemicals in the various pre and post-textile manufacturing processes has made the textile industry conscious of its negative contribution to environmental pollution. Popular environmentally friendly fibers such as recycled polyester and organic cotton have been now increasingly used by fabrics and apparel manufacturers. However, after these textiles or the finished apparel are manufactured, they have to be dyed in the same chemical dyes that are harmful and toxic to the environment. Dyeing is a major area of concern for the environment as well as for people who have chemical sensitivities as it may cause nausea, breathing difficulties, seizures, etc. Ayurvastra or herbal medical textiles are one step ahead of the organic lifestyle, which supports the core concept of holistic well-being and also eliminates the impact of harmful chemicals and pesticides. There is a wide range of herbs that can be used not only for dyeing but also for providing medicinal properties to the textiles like antibacterial, antifungal, antiseptic, antidepressant and for treating insomnia, skin diseases, etc. The concept of herbal dyeing of fabric is to manifest herbal essence in every aspect of clothing, i.e., from production to end-use, additionally to eliminate the impact of harmful chemical dyes and chemicals which are known to result in problems like skin rashes, headache, trouble concentrating, nausea, diarrhea, fatigue, muscle and joint pain, dizziness, difficulty breathing, irregular heartbeat and seizures. Herbal dyeing or finishing on textiles will give an extra edge to the textiles as it adds an extra function to the fabric. The herbal extracts can be applied to the textiles by a simple process like the pad dry cure method and mainly acts on the human body through the skin for aiding in the treatment of disease or managing the medical condition through its herbal properties. This paper, therefore, delves into producing Ayurvastra, which is a perfect amalgamation of cloth and wellness. The aim of the paper is to design and create herbal disposable and non-disposable medical textile products acting mainly topically (through the skin) for providing medicinal properties/managing medical conditions. Keeping that in mind, a range of antifungal socks and antibacterial napkins treated with turmeric and aloe vera were developed, which are recommended for the treatment of fungal and bacterial infections, respectively. Both Herbal Antifungal socks and Antibacterial napkins have proved to be efficient enough in managing and treating fungal and bacterial infections of the skin, respectively.

Keywords: ayurvastra, ayurveda, herbal, pandemic, sustainable

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Authors: Ashiq Hussain, Mahwash Hussain, Zeenat Parveen

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Now a days the demand for broadband service has increased. Due to which the researchers are trying to find a solution to provide a large amount of service. There is a shortage of bandwidth because of the use of downloading video, voice and data. One of the solutions to overcome this shortage of bandwidth is to provide the communication system with passive optical components. We have increased the data rate in this system. From experimental results we have concluded that the quality factor has increased by adding passive optical networks.

Keywords: WDM-PON, optical fiber, BER, Q-factor, eye diagram

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