Search results for: random differential inclusion

Authors: Newroz Nooralddin Abdulrazaq, Thuraya Mahmood Qaradaghi

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The McEliece cryptosystem is an asymmetric type of cryptography based on error correction code. The classical McEliece used irreducible binary Goppa code which considered unbreakable until now especially with parameter [1024, 524, and 101], but it is suffering from large public key matrix which leads to be difficult to be used practically. In this work Irreducible and Separable Goppa codes have been introduced. The Irreducible and Separable Goppa codes used are with flexible parameters and dynamic error vectors. A Comparison between Separable and Irreducible Goppa code in McEliece Cryptosystem has been done. For encryption stage, to get better result for comparison, two types of testing have been chosen; in the first one the random message is constant while the parameters of Goppa code have been changed. But for the second test, the parameters of Goppa code are constant (m=8 and t=10) while the random message have been changed. The results show that the time needed to calculate parity check matrix in separable are higher than the one for irreducible McEliece cryptosystem, which is considered expected results due to calculate extra parity check matrix in decryption process for g2(z) in separable type, and the time needed to execute error locator in decryption stage in separable type is better than the time needed to calculate it in irreducible type. The proposed implementation has been done by Visual studio C#.

Keywords: McEliece cryptosystem, Goppa code, separable, irreducible

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Showkat Ahmad Lone, Ahmadur Rahman, Ariful Islam

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The major assumption in accelerated life tests (ALT) is that the mathematical model relating the lifetime of a test unit and the stress are known or can be assumed. In some cases, such life–stress relationships are not known and cannot be assumed, i.e. ALT data cannot be extrapolated to use condition. So, in such cases, partially accelerated life test (PALT) is a more suitable test to be performed for which tested units are subjected to both normal and accelerated conditions. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests using progressive failure-censored hybrid data with random removals. The life data of the units under test is considered to follow exponential life distribution. The removals from the test are assumed to have binomial distributions. The point and interval maximum likelihood estimations are obtained for unknown distribution parameters and tampering coefficient. An optimum test plan is developed using the D-optimality criterion. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm.

Keywords: binomial distribution, d-optimality, multiple censoring, optimal design, partially accelerated life testing, simulation study

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Authors: Farah Amalina Mohamed Affandi, Redhwan Ahmad Al-Naggar, Seok Mui Wang, Thanikasalam Kathiresan

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Gestational choriocarcinoma is a condition in which there is an abnormal growth or a tumor inside the women’s uterus after conception. It is a type of gestational trophoblastic disease which is relatively rare and malignant. The current epidemiological data of this disease are inadequate. The purposes of this study are to examine the epidemiology of choriocarcinoma and their risk factors based on all available population-based and hospital-based data of the disease. In this study, we searched The MEDLINE and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using the keywords ‘choriocarcinoma’, ‘gestational’, ‘gestational choriocarcinoma’ and ‘epidemiology’. We included only human studies published in English between 1995 and 2015 to ensure up to date evidence. Case studies, case reports, animal studies, letters to the editor, news, and review articles were excluded. Retrieved articles were screened in three phases. In the first phase, any articles that did not match the inclusion criteria based solely on titles were excluded. In the second phase, the abstracts of remaining articles were screened thoroughly; any articles that did not meet our inclusion criteria were excluded. In the final phase, full texts of the remaining articles were read and assessed to exclude articles that did not meet the inclusion criteria or any articles that fulfilled the exclusion criteria. Duplicates articles were also removed. Systematic reviews and meta-analysis were excluded. Extracted data were summarized in table and figures descriptively. The reference lists of included studies were thoroughly reviewed in search for other relevant studies. A total of ten studies met all the selection criteria. Nine were retrospective studies and one was cohort study. Total numbers of 4563 cases of choriocarcinoma were reviewed from several countries which are Korea, Japan, South Africa, USA, New Mexico, Finland, Turkey, China, Brazil and The Netherlands. Different studies included different range of age with their mean age of 28.5 to 30.0 years. All studies investigated on the disease’s incidence rate, only two studies examined on the risk factors or associations of the disease. Approximately 20% of the studies showed a reduction in the incidence of choriocarcinoma while the other 80% showed inconsistencies in rate. Associations of age, fertility age, occupations and socio-demographic with the status remains unclear. There is limited information on the epidemiological aspects of gestational choriocarcinoma. The observed results indicated there was a decrease in the incidence rate of gestational choriocarcinoma globally. These could be due to the reduction in the incidence of molar pregnancy and the efficacy of the treatment, mainly by chemotherapy.

Keywords: epidemiology, gestational choriocarcinoma, incidence, prevalence, risk factor

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Authors: Aysheh Maslemani, Ruth Mayo, Greg Maio, Ariel Knafo-Noam

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Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study, we aim to examine this possibility. Four hundred participants over 18 years(M=41.6, SD=13.7, Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them and the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence.

Keywords: emotion, social cognition, values., ambivalence

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Authors: Aysheh Maslemani, Ruth Mayo, Greg Maio, Ariel Knafo-Noam

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Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study we aim to examine this possibility. Four hundred participants over 18 years(M=41.6,SD=13.7,Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept, and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them, the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence.

Keywords: ambivalence, emotion, social cognition, values

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Authors: Suleman Ibrahim

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Purpose: The research is seeking people's perceptions on cybercrime issues, rather than their knowledge of the facts. Unlike the Tripartite Cybercrime Framework (TCF), the binary models are ill-equipped to differentiate between cyber fraud (a socioeconomic crime) and cyber bullying or cyber stalking (psychosocial cybercrimes). Whilst the binary categories suggested that digital crimes are dichotomized: (i.e. cyber-enabled and cyber-dependent), the TCF, recently proposed, argued that cybercrimes can be conceptualized into three groups: socioeconomic, psychosocial and geopolitical. Concomitantly, as regards to the experience/perceptions of cybercrime, the TCF’s claim requires substantiation beyond its theoretical realm. Approach/Methodology: This scholar endeavor framed with the TCF, deploys a survey method to explore the experience of cybercrime across gender. Drawing from over 400 participants in the UK, this study aimed to contrast the differential perceptions/experiences of socioeconomic cybercrime (e.g. cyber fraud) and psychological cybercrime (e.g. cyber bullying and cyber stalking) across gender. Findings: The results revealed that cyber stalking was rated as least serious of the different digital crime categories. Further revealed that female participants judged all types of cybercrimes as more serious than male participants, with the exception of socioeconomic cybercrime – cyber fraud. This distinction helps to emphasize that gender cultures and nuances not only apply both online and offline, it emphasized the utilitarian value of the TCF. Originality: Unlike existing data, this study has contrasted the differential perceptions and experience of socioeconomic and psychosocial cybercrimes with more refined variables.

Keywords: gender variations, psychosocial cybercrime, socioeconomic cybercrime, tripartite cybercrime framework

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Authors: Behnam Zahednejad, Saeed Kosari

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Widespread deployment of Internet of Things (IoT) has raised security and privacy issues in this environment. Designing a secure two-factor authentication scheme between the user and server is still a challenging task. In this paper, we focus on Cancelable Biometric (CB) as an authentication factor in IoT. We show that previous CB-based scheme fail to provide real two-factor security, Perfect Forward Secrecy (PFS) and suffer database attacks and traceability of the user. Then we propose our improved scheme based on CB and Physically Unclonable Functions (PUF), which can provide real two-factor security, PFS, user’s unlinkability, and resistance to database attack. In addition, Key Compromise Impersonation (KCI) resilience is achieved in our scheme. We also prove the security of our proposed scheme formally using both Real-Or-Random (RoR) model and the ProVerif analysis tool. For the usability of our scheme, we conducted a performance analysis and showed that our scheme has the least communication cost compared to the previous CB-based scheme. The computational cost of our scheme is also acceptable for the IoT environment.

Keywords: IoT, two-factor security, cancelable biometric, key compromise impersonation resilience, perfect forward secrecy, database attack, real-or-random model, ProVerif

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Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

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There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

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Authors: Chulsang Yoo, Gildo Kim

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Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012).

Keywords: CSU-HIDRO algorithm, dual polarization radar, JPOLE algorithm, radar rainfall estimation algorithm

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Authors: Ashok Kalagura

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This paper presents a data-mining model for fault-zone identification of flexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines.

Keywords: distance relaying, fault-zone identification, random forests, RFs, support vector machine, SVM, thyristor-controlled series compensator, TCSC, unified power-flow controller, UPFC

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Authors: Marija Zulić, Vanda Hájková, Nina Brkić-Jovanović, Linda Rathousová, Sanja Tomić

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Cerebral palsy is primarily reflected in the disorder of the development of movement and posture, which may be accompanied by sensory disturbances, disturbances of perception, cognition and communication, behavioural disorders and epilepsy. According to current inclusive attitudes towards people with disabilities implies that full social participation of children with cerebral palsy means inclusion in all activities in family, peer, school and leisure environments in the same scope and to the same extent as is the case with the children of proper development and without physical difficulties. Due to the fact that it has been established that the quality of children's participation in primary school is directly related to their social inclusion in future life, the aim of the paper is to identify predictors of social participation, respectively, and in particular, factors that could to improve the quality of social participation of children with cerebral palsy, in the primary school environment in Czech Republic. The study includes children with cerebral palsy (n = 75) in the Czech Republic, aged between six and 12 years who attend mainstream or special primary schools to the sixth grade. The main instrument used was the first and third part of the School function assessment questionnaire. It will also take into account the type of damage assessed according to a scale the Gross motor function classification system, five–level classification system for cerebral palsy. The research results will provide detailed insight into the degree of social participation of children with cerebral palsy and the factors that would be a potential cause of their levels of participation, in regular and special primary schools, in different socioeconomic environments in Czech Republic.

Keywords: cerebral palsy, Czech republic, social participation, the school function assessment

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Authors: Santony Santony, Teguh Rahayu Sartono, Iin Noor Chozin

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Introduction: Pneumonia is acute inflammation on lung parenchyma which is caused by bacteria, virus, fungi, or parasite. In Indonesia, Pneumonia is among the ten inpatient cases. Length of stay is related to the increased morbidity rate, nosocomial infection, and costs. The aim of this study is to assess the effect of pulmonary rehabilitation on the difference in length of stay and the level of Interleukin 6 (IL-6) as an inflammation biomarker for community-acquired pneumonia (CAP) patients in non-intensive rooms. Therefore, pulmonary rehabilitation as adjunctive therapy can be routinely exercised in order to shorten the length of stay, along with the decrease in IL-6 level. Methods: This study was conducted from May to October 2019 at Saiful Anwar General Hospital, Malang. 40 community-acquired pneumonia patients in non-intensive rooms were divided into two groups. 20 patients in the treatment group and 20 patients in the control group, all of them were selected through both inclusion and exclusion criteria. This study used simple consecutive random sampling. In the treatment group, pulmonary rehabilitation performed was composed of breathing exercise, effective coughing technique, clapping (percussion), postural drainage, as well as respiratory muscle training using incentive spirometry device. Pulmonary rehabilitation was conducted twice over five days with a minimum duration of 15 minutes. Blood samples were taken both on the first and the fifth day of the treatment to measure IL-6 level as an inflammation biomarker. Result: For the treatment group, the length of stay was 5.35 days whereas the control group 7.6 days. It can be seen that the treatment group had a shorter length of stay by 2.25 days (P<0,001). The IL-6 level on the first day for the treatment group was 36.27 pg/ml, whereas on the fifth day was 34.36 pg/ml. There was a decrease in IL-6 level on the fifth day of treatment even though it was not statistically significant (P=0.628). IL-6 level on the control group for the first day was 67.76 pg/ml, and after the fifth day, the level decreased to 54.43 pg/ml. There seemed to be a decrease in the IL-6, but it was not statistically significant (P=0.502). On the fifth day, the treatment group showed an average IL-6 level of 34.36 pg/ml. This value was lower than that of the control group which did not receive pulmonary rehabilitation having an IL-6 level of 54.43 pg/ml, even though it was not statistically significant (p=0.221). Conclusion: This study concluded that pulmonary rehabilitation as an adjunctive therapy shortened length of stay by 2.25 days for community-acquired pneumonia patients in a non-intensive room. Both groups experienced a decrease in IL-6 level on the fifth day in comparison with the first day even though it was not statistically significant P>0,05. IL-6 level as an inflammation biomarker decreased on the fifth day of treatment which was in accordance with improvement on pneumonia patients.

Keywords: community-acquired pneumonia, interleukin-6, length of stay, pulmonary rehabilitation

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Authors: Ashima Sharma, Tapan K. Chaudhuri

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Human Serum Albumin (HSA) is one of the most demanded therapeutic protein with immense biotechnological applications. The current source of HSA is human blood plasma. Blood is a limited and an unsafe source as it possesses the risk of contamination by various blood derived pathogens. This issue led to exploitation of various hosts with the aim to obtain an alternative source for the production of the rHSA. But, till now no host has been proven to be effective commercially for rHSA production because of their respective limitations. Thus, there exists an indispensable need to promote non-animal derived rHSA production. Of all the host systems, Escherichia coli is one of the most convenient hosts which has contributed in the production of more than 30% of the FDA approved recombinant pharmaceuticals. E. coli grows rapidly and its culture reaches high cell density using inexpensive and simple substrates. The fermentation batch turnaround number for E. coli culture is 300 per year, which is far greater than any of the host systems available. Therefore, E. coli derived recombinant products have more economical potential as fermentation processes are cheaper compared to the other expression hosts available. Despite of all the mentioned advantages, E. coli had not been successfully adopted as a host for rHSA production. The major bottleneck in exploiting E. coli as a host for rHSA production was aggregation i.e. majority of the expressed recombinant protein was forming inclusion bodies (more than 90% of the total expressed rHSA) in the E. coli cytosol. Recovery of functional rHSA form inclusion body is not preferred because it is tedious, time consuming, laborious and expensive. Because of this limitation, E. coli host system was neglected for rHSA production for last few decades. Considering the advantages of E. coli as a host, the present work has targeted E. coli as an alternate host for rHSA production through resolving the major issue of inclusion body formation associated with it. In the present study, we have developed a novel and innovative method for enhanced soluble and functional production of rHSA in E.coli (~60% of the total expressed rHSA in the soluble fraction) through modulation of the cellular growth, folding and environmental parameters, thereby leading to significantly improved and enhanced -expression levels as well as the functional and soluble proportion of the total expressed rHSA in the cytosolic fraction of the host. Therefore, in the present case we have filled in the gap in the literature, by exploiting the most well studied host system Escherichia coli which is of low cost, fast growing, scalable and ‘yet neglected’, for the enhancement of functional production of HSA- one of the most crucial biomolecule for clinical and biotechnological applications.

Keywords: enhanced functional production of rHSA in E. coli, recombinant human serum albumin, recombinant protein expression, recombinant protein processing

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Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

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Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: Idowu Kemi Ruth, Adeyemo Adedayo Akinade, Iyanda Adegboyega Ibukun, Idowu Olubukola Precious Akinade

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Liveability and mortality rate is a germane aspect of product performance that cannot be overlooked in poultry production, while the disease is a major threat in the poultry industry which can cause a major loss for the farmer and a reduction in the total income generated from the stock. Therefore, efforts must be made to enhance the health status of chickens to reduce mortality. The study was conducted to investigate the effect of different biotic additives (prebiotic, probiotic and synbiotic ) on the performance of Noiler females at the growing phase (forty-nine days) till the point of the first egg across the biotic additive. A total of one hundred and twenty-eight female Noiler were used for the experiment. Experimental treatment consisted of prebiotic, probiotic, synbiotic and control at the inclusion rate of a gram into a kilogram of feed. Parameters measured are Feed intake, feed conversion ratio, the weight of the first egg, age of the first egg and livability. Data collected were subjected to a one-way analysis of variance. The result obtained revealed a better growth performance across the treatments than the control group with the least final weight at nineteen weeks of point of lay. Prebiotic treatment had the best age at first lay on day one hundred and thirty seven followed by other treatments on day one hundred and fifty four. However, the size of the eggs was not significantly influenced by the biotic additive. Hence, the experiment can be concluded that the inclusion of different biotic additives influenced the growth performance; likewise, the Prebiotic had a significant effect on the age of first laying in Noiler chicken, and livability was a hundred percent throughout the duration of the experiment.

Keywords: prebiotic, probiotic, synbiotic, noiler

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Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Hajer Habib, Ghazi Boulila

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The objective of this paper is to present our contribution to the theoretical literature through a simple theoretical model dealing with the effect of transferring funds on the labor market of the countries of origin and on the other hand to test this relationship empirically in the case of Tunisia. The methodology used consists of estimating a panel of the nine main destinations of the Tunisian diaspora in Europe between 1994 and 2014 in order to better value the net effect of these migratory financial flows on unemployment through population growth. The empirical results show that the main factors explaining the decision to emigrate are the economic factors related mainly to the income differential, the demographic factors related to the differential age structure of the origin and host populations, and the cultural factors linked basically to the mastery of the language. Indeed, the stock of migrants is one of the main determinants of the transfer of migratory funds to Tunisia. But there are other variables that do not lack importance such as the economic conditions linked by the host countries. This shows that Tunisian migrants react more to economic conditions in European countries than in Tunisia. The economic situation of European countries dominates the numbers of emigrants as an explanatory factor for the amount of transfers from Tunisian emigrants to their country of origin. Similarly, it is clear that there is an indirect effect of transfers on unemployment in Tunisia. This suggests that the demographic transition conditions the effects of transferring funds on the level of unemployment.

Keywords: demographic changes, international migration, labor market, remittances

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Authors: Jeremie Coullon, Robert J. Webber

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We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

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Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Vilar, Katila

Abstract:

Rapid urbanization have made of spontaneous settlements one of the dominant´s social subjects of the XXIst century. Currently, it´s recognized that these territories cannot easily be eradicated and are a way of life to many populations of emergent countries. Since late 90s, there is an urgent concern in finding planning and efficient urban design strategies to poverty reduction, spatial integration and social inclusion of low-income communities. The article aims to identify, understand and evaluate the social inclusion´s processes through the urban transformation that has been undertaken in Moravia and how they affected the community´s social capital. To achieve this objective, we start to analyse the PPMIM´s planning discourse in which prevails the sustainability´s concept, to further identify, through the analysis of the project carried out, the urban design strategies implemented and their impact on the perception and on the community´s experience, and, finally, how these focused on the social capital. It relies on concepts such as urban design, social capital, local development and sustainability. At the urban design level it starts on the current principles of “making places”, on the new urbanism concepts and on the practices on the ground carried out by a new generation of architects/planners whose have the main ethical approach in order to create more opportunities and greater social impact to these territories. At the social capital´s level and on the development´s theory, relies on authors such as Coleman, Putman Kliksberg and Amartya Sen. Finally, it aims to address a general discussion about the positive and negative implications of slum upgrading programmes and some necessary recommendations for urban design and social capital can really be translated into real resources for the self sustainable development of low-income communities and their future generations.

Keywords: local and sustainable development, social capital, spontaneous settlements, urban design

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Lo Kai Chung

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Fathers in the family of disabled children play a crucial role in fostering child development. Previous studies addressing emotions of father involvement in rearing children with special needs have been rare. With reference to the cultural orientation and masculine idea of Chinese fathers, shame and guilt are probable causal emotions that affect fathers’ psycho-behavioral reactions and, thus, father involvement. Based on the findings of our earlier qualitative studies, the current study aims to develop and validate a multi-item scale of guilt or shame and explore their relations with and fatherhood in families with children with special needs. A model is proposed to understand the roles that shame and guilt play in affecting fathers’ involvement in their family system. The severity and type of the child’s special needs are regarded as independent variables affecting the father’s emotional responses – shame and guilt. It is hypothesized that shame and guilt, under the influence of masculinity, lead to avoidance and compensation, respectively, which subsequently decrease and increase father involvement with children with special needs. A cross-sectional online questionnaire survey of fathers with children with special needs recruited by convenience sampling was conducted. Potential participants were reached by bulk emails, related groups on the Internet and education/social services providers. Totally 537 valid sets of online questionnaires were collected from fathers of children with special needs. EFA on the items pool of shame and guilt was performed, resulting in an x-item single-factor solution and y-item single-factor solution, respectively. Further path model analysis revealed that shame and guilt, under the influence of masculinity, showed differential avoidance and compensation responses and resulted in a decrease and increase in father involvement with special needs children. Demographic and key confounding variables were controlled in the analysis. The shame and guilt scales developed show good psychometric properties. Furthermore, they showed significant differential impacts, under the influence of masculinity, on avoidance and compensation behaviours, consequently resulting in a decrease/increase in father involvement in the expected directions. The findings have important theoretical and practical implications. At the community and policy level, the findings inform the design of strategies for strengthening the role of men in families with special needs children.

Keywords: emotions, father involvement, guilt, shame, special needs

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Authors: Helga Martins, Idália Matias

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Background: Dementia is an important research topic since that life expectancy worldwide is increasing, so people are getting older. The aging of populations has a major impact on the increase in dementia, and nurses play a major role in taking care of these patients. Therefore, the implementation of nursing interventions based on evidence is vital so that we are aware of what we can do in clinical practice in order to provide patient cantered care to patients with dementia. Aim: To identify the nurse´s interventions in patients with dementia during clinical practice. Method: Literature review grounded on an electronic search in the EBSCOhost platform (CINAHL Plus with Full Text, MEDLINE with Full Text, and Nursing & Allied Health Collection), using the search terms of "dementia" AND "nurs*" AND “interventions” in the abstracts. The inclusion criteria were: original papers published up to June 2021. A total of 153 results after de duplicate removal we kept 104. After the application of the inclusion criteria, we included 15 studies This literature review was performed by two independent researchers. Results: A total of 15 results about nurses’ interventions in patients with dementia were included in the study. The major interventions are therapeutic communication strategies, environmental management of stressors involving family/caregivers; strategies to promote patient safety, and assistance in activities of daily living in patients who are clinically deteriorated. Conclusion: Taking care of people with dementia is a complex and demanding task. Nurses are required to have a set of skills and competences in order to provide nursing interventions. We highlight that is necessary an awareness in nursing education regarding providing nursing care to patients with dementia.

Keywords: dementia, interventions, nursing, review

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Authors: Teh Raihana Nazirah Roslan, Siti Zulaiha Ibrahim, Sharmila Karim

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A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study.

Keywords: Cox-Ingersoll-Ross model, equity warrants, Heston model, hybrid models, stochastic

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Authors: Rikson Gultom

Abstract:

Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Melike Aydoğan, Dürdane Öztürk

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Let H(D) be the linear space of all analytic functions defined on the open unit disc. A log-harmonic mappings is a solution of the nonlinear elliptic partial differential equation where w(z) ∈ H(D) is second dilatation such that |w(z)| < 1 for all z ∈ D. The aim of this paper is to define some inequalities of starlike logharmonic functions of order α(0 ≤ α ≤ 1).

Keywords: starlike log-harmonic functions, univalent functions, distortion theorem

Procedia PDF Downloads 525

Authors: Bennani Zoubir Nada, El Hiri Abderrazak, El Hajri Aimad

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In context of economic and health crisis, sustainable entrepreneurship has become one of the best solutions to economic recovery. This study is about the third program of the Moroccan national human development initiative in her third phase which began in 2019 and continuous until 2023, and which deals with income improvement and social inclusion of young people, under the high patronage of his majesty the king of Morocco. What is the approach of this program and how entrepreneurship training of young people can be a pillar to generate income and create jobs? Starting on the effectuation theory, we adopted an exploratory qualitative approach through semi-structured interviews with national human development initiative stakeholders in the area of Meknes-Morocco, which allowed us the state of progress of this program. We carried out a survey based on a grid of questions to collect information that we processed using NVIVO software. The most relevant results are that people eligible are jobless young people, who are between 18 and 35 years old, who reside in Meknes and surroundings and who have a project idea. They are trained by experts in entrepreneurship and management through targeted and diversified courses. To ensure the sustainability of projects, the project organisers have provided measures to ensure the sustainability of the companies through continuous monitoring and evaluation as well as support during all phases from the project idea to the realisation and progress.

Keywords: sustainable entrepreneurship, training, social inclusion, national human development initiative in Morocco (INDH), youth entrepreneurship, the effectuation theory

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Authors: Madhu Aneja, Sapna Sharma

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Thermal conductivity of ordinary heat transfer fluids is not adequate to meet today’s cooling rate requirements. Nanoparticles have been shown to increase the thermal conductivity and convective heat transfer to the base fluids. One of the possible mechanisms for anomalous increase in the thermal conductivity of nanofluids is the Brownian motions of the nanoparticles in the basefluid. In this paper, the natural convection of incompressible nanofluid over a nonlinear stretching sheet in the presence of magnetic field is studied. The flow and heat transfer induced by stretching sheets is important in the study of extrusion processes and is a subject of considerable interest in the contemporary literature. Appropriate similarity variables are used to transform the governing nonlinear partial differential equations to a system of nonlinear ordinary (similarity) differential equations. For computational purpose, Finite Element Method is used. The effective thermal conductivity and viscosity of nanofluid are calculated by KKL (Koo – Klienstreuer – Li) correlation. In this model effect of Brownian motion on thermal conductivity is considered. The effect of important parameter i.e. nonlinear parameter, volume fraction, Hartmann number, heat source parameter is studied on velocity and temperature. Skin friction and heat transfer coefficients are also calculated for concerned parameters.

Keywords: Brownian motion, convection, finite element method, magnetic field, nanofluid, stretching sheet

Procedia PDF Downloads 218