Search results for: emotional intelligence

Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Khalid Obaed Mahmod, Mesut Cevik

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The term smart city has appeared recently and was accompanied by many definitions and concepts, but as a simplified and clear definition, it can be said that the smart city is a geographical location that has gained efficiency and flexibility in providing public services to citizens through its use of technological and communication technologies, and this is what distinguishes it from other cities. Smart cities connect the various components of the city through the main and sub-networks in addition to a set of applications and thus be able to collect data that is the basis for providing technological solutions to manage resources and provide services. The basis of the work of the smart city is the use of artificial intelligence and the technology of the Internet of Things. The work presents the concept of smart cities, the pillars, standards, and evaluation indicators on which smart cities depend, and the reasons that prompted the world to move towards its establishment. It also provides a simplified hypothetical way to measure the ideal smart city model by defining some indicators and key pillars, simulating them with logic circuits, and testing them to determine if the city can be considered an ideal smart city or not.

Keywords: factors, indicators, logic gates, pillars, smart city

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Authors: John G. Vongas, Raghid Al Hajj

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We investigated the effects of winning and losing on men’s testosterone and assessed whether androgen reactivity affected their empathic accuracy and their aggression. We also explored whether their power motivation would moderate the relationships between competitive, hormonal, and behavioral outcomes. In Experiment 1, 84 males competed on a task that allegedly gauged their leadership potential and future earnings, after which they interpreted people’s emotional expressions. Results showed that winners were more capable of accurately inferring others’ emotions compared to losers and this ability improved with increasing power. Second, testosterone change mediated the relationship between competitive outcomes and empathic accuracy, with post-competitive testosterone increases relating to more accuracy. In Experiment 2, 72 males again competed after which they were measured on two aggression subtypes: proactive and reactive. Results showed that neither the competitive outcome nor the testosterone change had a significant effect on either types of aggression. However, as power increased, winners aggressed more proactively than losers whereas losers aggressed more reactively than winners. Finally, in both experiments, power moderated the relationship between competitive outcomes and testosterone change. Collectively, these studies add to existing research that explores the psychophysiological effects of competition on individuals’ empathic and aggressive responses.

Keywords: competition, testosterone, power motivation, empathic accuracy, proactive aggression, reactive aggression

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Authors: Mohammadhadi Mirmohammadi, Reza Safian, Hossein Haddad

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This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented.

Keywords: derivative-free optimization, Improving Hit and Run method, real-coded genetic algorithm, rolling schedules of tandem cold rolling mill

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Authors: Salam Hadid, Mohammad Khatib

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The International Council of Nurses-ICN defined nursing as a sphere integrating autonomous and collaborative care intended for the individual, family and community within and outside of the care setting. Nursing as a care profession has developed broadly over recent decades in terms of its essentials, expertise and primarily academically. Despite the impressive growth of the profession, there is still extreme diversity in the public’s perceptions and opinions of the profession and its professionals and in the knowledge on the fundamentals of its true function and spheres of engagement. The current study examines the existing knowledge among the general population regarding the nursing profession. The population consisted of 498 respondents, 236 women and 262 men, age 18-81. The respondents noted that nursing focuses on the technical, and the emotional aspects and promotion of health for the patient are not the nurse’s responsibility. Most of the respondents saw nurses working mainly in hospital and community-based clinic settings. They considered nursing to be a high prestige profession in general, but less prestigious among respondents exposed to healthcare provision. Most of the respondents considered nursing to be a humane profession but without independence and with no need for academic studies. The findings are incompatible with the definition of nursing and its spheres of action as defined in the ICN Code of Ethics. Two suggestions are to work through nursing schools addressing the student nurses, as ambassadors for the profession. The second is using the healthcare encounter between the nursing staff and the public to improve the image of nurses.

Keywords: ethics, nurse image, public, nursing

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Authors: Farzaneh Badinloo, Niloufar Jalali-Moghadam, Reza Kormi-Nouri

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The aim of this study was to explore the short-term and working memory performances across age and gender in school aged children. Most of the studies have been interested in looking into memory changes in adult subjects. This study was instead focused on exploring both short-term and working memories of children over time. Totally 410 school child participants belonging to four age groups (approximately 8, 10, 12 and 14 years old) among which were 201 girls and 208 boys were employed in the study. digits forward and backward tests of the Wechsler children intelligence scale-revised were conducted respectively as short-term and working memory measures. According to results, there was found a general increment in both short-term and working memory scores across age (p ˂ .05) by which whereas short-term memory performance was shown to increase up to 12 years old, working memory scores showed no significant increase after 10 years old of age. No difference was observed in terms of gender (p ˃ .05). In conclusion, this study suggested that both short-term and working memories improve across age in children where 12 and 10 years of old are likely the crucial age periods in terms of short-term and working memories development.

Keywords: age, gender, short-term memory, working memory

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: kaveh Jalilzadeh, Maryam Rastgari

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Motivation for learning a foreign language is needed for learners of any foreign language to effectively learn language skills. However, there are some factors that lead to the learners’ demotivation. Therefore, teachers of foreign languages, most notably English language which turned out to be an international language for academic and business purposes, need to be well aware of the demotivation sources and know how to reduce learners’ demotivation. This study is an attempt to explore demotivation-reducing strategies employed by Turkish EFL learners in L2 writing. The researchers used a qualitative case study and employed semi-structured interviews to collect data. The informants recruited in this study were 20 English writing lecturers who were selected through purposive sampling among university lecturers/instructors at the state and non-state universities in Istanbul and Ankara. Interviews were transcribed verbatim, and MAXQDA software (version 2022) was used for performing coding and thematic analysis of the data. Findings revealed that Turkish EFL teachers use 18 strategies to reduce language learners’ demotivation. The most frequently reported strategies were: writing in a group, writing about interesting topics, writing about new topics, writing about familiar topics, writing about simple topics, and writing about relevant topics. The findings have practical implications for writing teachers and learners of the English language.

Keywords: phenomenological study, emotional vulnerability, motivation, digital Settings

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Authors: Shahbaz G. Hassan

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Water quality models are very important to predict the changes in surface water quality for environmental management. The aim of this paper is to give an overview of the water qualities, and to provide directions for selecting models in specific situation. Water quality models include one kind of model based on a mechanistic approach, while other models simulate water quality without considering a mechanism. Mechanistic models can be widely applied and have capabilities for long-time simulation, with highly complexity. Therefore, more spaces are provided to explain the principle and application experience of mechanistic models. Mechanism models have certain assumptions on rivers, lakes and estuaries, which limits the application range of the model, this paper introduces the principles and applications of water quality model based on the above three scenarios. On the other hand, mechanistic models are more easily to compute, and with no limit to the geographical conditions, but they cannot be used with confidence to simulate long term changes. This paper divides the empirical models into two broad categories according to the difference of mathematical algorithm, models based on artificial intelligence and models based on statistical methods.

Keywords: empirical models, mathematical, statistical, water quality

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Authors: Igor A. Krichtafovitch

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The evolutionary processes are not linear. Long periods of quiet and slow development turn to rather rapid emergences of new species and even phyla. During Cambrian explosion, 22 new phyla were added to the previously existed 3 phyla. Contrary to the common credence the natural selection or a survival of the fittest cannot be accounted for the dominant evolution vector which is steady and accelerated advent of more complex and more intelligent living organisms. Neither Darwinism nor alternative concepts including panspermia and intelligent design propose a satisfactory solution for these phenomena. The proposed hypothesis offers a logical and plausible explanation of the evolutionary processes in general. It is based on two postulates: a) the Biosphere is a single living organism, all parts of which are interconnected, and b) the Biosphere acts as a giant biological supercomputer, storing and processing the information in digital and analog forms. Such supercomputer surpasses all human-made computers by many orders of magnitude. Living organisms are the product of intelligent creative action of the biosphere supercomputer. The biological evolution is driven by growing amount of information stored in the living organisms and increasing complexity of the biosphere as a single organism. Main evolutionary vector is not a survival of the fittest but an accelerated growth of the computational complexity of the living organisms. The following postulates may summarize the proposed hypothesis: biological evolution as a natural life origin and development is a reality. Evolution is a coordinated and controlled process. One of evolution’s main development vectors is a growing computational complexity of the living organisms and the biosphere’s intelligence. The intelligent matter which conducts and controls global evolution is a gigantic bio-computer combining all living organisms on Earth. The information is acting like a software stored in and controlled by the biosphere. Random mutations trigger this software, as is stipulated by Darwinian Evolution Theories, and it is further stimulated by the growing demand for the Biosphere’s global memory storage and computational complexity. Greater memory volume requires a greater number and more intellectually advanced organisms for storing and handling it. More intricate organisms require the greater computational complexity of biosphere in order to keep control over the living world. This is an endless recursive endeavor with accelerated evolutionary dynamic. New species emerge when two conditions are met: a) crucial environmental changes occur and/or global memory storage volume comes to its limit and b) biosphere computational complexity reaches critical mass capable of producing more advanced creatures. The hypothesis presented here is a naturalistic concept of life creation and evolution. The hypothesis logically resolves many puzzling problems with the current state evolution theory such as speciation, as a result of GM purposeful design, evolution development vector, as a need for growing global intelligence, punctuated equilibrium, happening when two above conditions a) and b) are met, the Cambrian explosion, mass extinctions, happening when more intelligent species should replace outdated creatures.

Keywords: supercomputer, biological evolution, Darwinism, speciation

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Authors: Catarina Grande, Barbara Mota

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The main goal of the present study was to understand the impact of childhood cancer on the quality of life of survivors and the extent to which oncologic disease affects the functionality and participation of survivors at the present time, compared to the time of diagnosis. Six survivors of pediatric cancer participated in the study. Participants were interviewed using a semi-structured interview, adapted from two instruments present in the literature - QALY and QLACS - and piloted through a previous study. This study is based on a qualitative approach using content analysis, allowing the identification of categories and subcategories. Subsequently, the correspondence between the units of meaning and the codes in the International Classification of Functioning, Disability, and Health for Children and Young, which contributed to a more detailed analysis of the impact on the quality of life of survivors in relation to the domains under study. The results showed significant changes between the moment of diagnosis and the present moment, concretely at the microsystem of the survivor. Regarding functionality and participation, the results show that the functions of the body are the most affected domain, emphasizing the emotional component that currently has a greater impact on the quality of life of survivors. The present study allowed identifying a set of codes for the development of a CIF-CJ core set for pediatric cancer survivors. He also indicated the need for future studies to validate and deepen these issues.

Keywords: cancer, participation, quality of life, survivor

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Authors: Jennifer Sturgeon

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Stable relationships provide a predictable and trusting environment and are essential for early development, but high teacher turnover rates in childcare settings make it challenging for infants and toddlers to form stable relationships with their teachers. This can have an adverse effect on development and learning. The qualitative study discussed in this article draws from the experiences of early Head Start teachers and administrators to describe both the impact of teacher turnover and the motivational factors that contribute to teacher retention. A case study approach was used and included classroom observations, a review of exit interviews, and perceptions from focus groups of early Head Start staff in an urban early Head Start childcare center. Emerging from the case study was the discovery that teacher turnover has an impact on the social-emotional development of toddlers, particularly in self-regulation. Additional key findings that emerged include teacher turnover leading to negative effects on learning, a decrease in preschool preparation, and increased chaos in the classroom and center. Motivational factors that contributed to teacher retention included positive leadership, the mission to make a difference, and fair compensation.

Keywords: early childhood, teacher turnover, continuity of care, early head start

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Authors: Tessa Verhallen, Mama Africa

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By co-constructing personal narratives of sex workers in Namibia this paper represents how sex workers experience their violation of rights in Namibia. It is written from an emic (as an advisor for a sex worker-led organization named Rights not Rescue Trust) and an etic (as an ethnographer) point of view, in collaboration with the staff of the organization Rights not Rescue Trust. This organization represents circa 3000 members. The paper describes the current deplorable situation of sex workers in Namibia, encompassing the stigma and discrimination they face, their struggle to have their work decriminalized and their urge to advocate for human rights and the end of violations. Based on a triangular research design (ethnography, narratives, literature study, human rights’ training and counseling sessions) the authors show that sex workers, particularly LGBTI sex workers, are extremely vulnerable to emotional, physical, and sexual violence in Namibia. The main perpetrators of violence turn out to be not only clients and intimate partners but also law enforcement officers and health care workers who are supposed to protect and support sex workers. The sex workers’ narratives voice their disgraceful circumstances regarding how their rights are violated. It also highlights their importance to fight for their rights and access to health care, legal services and education in order to improve the sexual reproductive health of sex workers.

Keywords: HIV/aids, LGBTI, methodological innovative, sex work

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Authors: Emilia-Gheorghina Negru

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As an EU goal, gender diversity in early year’s education aims and promotes equality of chances and respect for gender peculiarities of the pupils which are involved in formal educational activities. Early year’s education, as the first step to the Curriculum, prints to teachers the need to identify the role of the gender dimension on this stage, depending on the age level of preschool children through effective, complex, innovative and analytical awareness of gender diversity teaching and management strategies. Through gender educational work we, as teachers, will examine the effectiveness of the PATHS (Promoting Alternative Thinking Strategies) curriculum the gender development of school-aged children. PATHS and a school-based preventive intervention model are necessary to be designed to improve children's ability to discuss and understand equality and gender concepts. Our teachers must create an intervention model and provide PATHS lessons during the school year. Results of the intervention will be effective for both low- and high-risk children in improving their range of math’s skills for girls and vocabulary, fluency and emotional part for boys in discussing gender experiences, their efficacy beliefs regarding the management of equality in gender area, and their developmental understanding of some aspects of gender.

Keywords: gender, gender differences, gender equality, gender role, gender stereotypes

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Authors: Célia Barreto Carvalho, Carolina da Motta, Marina Sousa, Joana Cabral, Ana Luísa Carvalho, Ermelindo Peixoto

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The aim of the current study was to develop and validate a Response to Stressful Situations Scale (RSSS) for the Portuguese population. This scale assesses the degree of stress experienced in scenarios that can constitute positive, negative and more neutral stressors, and also describes the physiological, emotional and behavioral reactions to those events according to their intensity. These scenario include typical stressor scenarios relevant to patients with schizophrenia, which are currently absent from most scale, assessing specific risks that these stressors may bring on subjects, which may prove useful in non-clinical and clinical populations (i.e. patients with mood or anxiety disorders, schizophrenia). Results from Principal Components Analysis and Confirmatory Factor Analysis of on two adult samples from general population allowed to confirm a three-factor model with good fit indices: χ2 (144)= 370.211, p = 0.000; GFI = 0.928; CFI = 0.927; TLI = 0.914, RMSEA = 0.055, P( rmsea ≤ 0.005) = 0.096; PCFI = 0.781. Further data analysis on the scale revealed that RSSS is an adequate assessment tool of stress response in adults to be used in further research and clinical settings, with good psychometric characteristics, adequate divergent and convergent validity, good temporal stability and high internal consistency.

Keywords: assessment, stress events, stress response, stress vulnerability

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Authors: Ava Salmanpour, Hanieh Saeedi, Payam Rouhi, Elahe Hamzeil, Shima Alimohammadi, Siamak Hossein Khalaj, Mohammad Asadian

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Software-defined network (SDN) is a network architecture designed to control network using software application in a central manner. This ability enables remote control of the whole network regardless of the network technology. In fact, in this architecture network intelligence is separated from physical infrastructure, it means that required network components can be implemented virtually using software applications. Today, power networks are characterized by a high range of complexity with a large number of intelligent devices, processing both huge amounts of data and important information. Therefore, reliable and secure communication networks are required. SDNs are the best choice to meet this issue. In this paper, SDN networks capabilities and characteristics will be reviewed and different basic controllers will be compared. The importance of using SDNs to escalate efficiency and reliability in utility power networks is going to be discussed and the comparison between the SDN-based power networks and traditional networks will be explained.

Keywords: software-defined network, SDNs, utility network, open flow, communication, gas and electricity, controller

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Authors: Lin Wenli, Fu Linchun, Zhang Yi, Wu Ming

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To meet the demands of long-life and high-intelligence for satellites, the electrical power system should be provided with self-health condition evaluation capability. Any over-stress events in operations should be recorded. Based on Linear stresses accumulation damage theory, accumulative damage analysis was performed on thermal-mechanical-electrical united stresses for three components including the solar array, the batteries and the power conditioning unit. Then an overall health percentage evaluation model for satellite electrical power system was built. To obtain the accurate quantity for system health percentage, an automatic feedback closed-loop correction method for all coefficients in the evaluation model was present. The evaluation outputs could be referred as taking earlier fault-forecast and interventions for Ground Control Center or Satellites self.

Keywords: satellite electrical power system, health percentage, linear stresses accumulation damage, evaluation model

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Husain S. Yawer, Vasim Raja Panwar, Nidhi Priya

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The objective of this study is to evaluate and compare the role of nano-gelatin and Bioengineered Scaffolds on the attachment, proliferation, and osteogenic differentiation of human dental pulp stem cells (DPSCs). Tooth decay and early fall have each been one of the most prevailing dental disorders which cause physical and emotional suffering and compromise the patient's quality of life. The design of novel scaffolding materials will be based on mimicking the architecture of natural dental extracellular matrix which may provide as in vivo environments for proper cell growth. This methodology will involve the combination of nano-fibred gelatin as well as biodegradable hydrogel based tooth scaffold. We have measured and optimized the Dental Pulp Stem Cells growth profile in cultures carried out on collagen-coated plastic surface, however, for tissue regeneration study, we aim to develop an enhanced microenvironment for stem cell growth and dental tissue regeneration. We believe biomimetic cell adhesion and scaffolds might provide a near in vivo growth environment for proper growth and differentiation of human DPSCs, which further help in dentin/pulp tissue regeneration.

Keywords: nano-gelatin, stem cells, dental pulp, scaffold

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Authors: Yosra Mefteh Rekik

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A computational agent-based model of financial markets stresses interactions and dynamics among a very diverse set of traders. The growing body of research in this area relies heavily on computational tools which by-pass the restrictions of an analytical method. The main goal of this research is to understand how the stock market operates and behaves how to invest in the stock market and to study traders’ behavior within the context of the artificial stock markets populated by heterogeneous agents. All agents are characterized by adaptive learning behavior represented by the Artificial Neuron Networks. By using agent-based simulations on artificial market, we show that the existence of heterogeneous agents can explain the price dynamics in the financial market. We investigate the relation between market diversity and market efficiency. Our empirical findings demonstrate that greater market heterogeneity play key roles in market efficiency.

Keywords: agent-based modeling, artificial stock market, heterogeneous expectations, financial stylized facts, computational finance

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Authors: Simon Mwima, Kasule Solomon Kibirige, Evans Jennifer Mann, Bosco Mukuba, Edson Chipalo, Agnes Nzomene, Eusebius Small, Moses Okumu

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Introduction: Social workers must create, implement, and evaluate client-centered psychosocial interventions (CCPI) to reduce the impact of intersectional stigma on HIV service utilization among sexual minorities. We contribute to the scarcity of evidence about sexual minorities in Uganda by using social support theory to explore clients' perceptions that shape CCPI. Based on Focused Group Discussion (FGD) with 31 adolescents recruited from Kampala's HIV clinics in 2021, our findings reveal the positive influence of instrumental, informational, esteem, emotional, and social network support as intersectional stigma reduction interventions. Men who have sex with men, lesbians, and bisexual women used such strategies to navigate a heavily criminalized and stigmatizing setting during the COVID-19 pandemic in Uganda. Conclusion: This study provides evidence for the social work profession to develop and implement psychosocial interventions that reduce HIV stigma and discrimination among MSM, lesbians, and bisexual young people living with HIV in Uganda.

Keywords: pyschosocial interventions, social work, intersectional stigma, HIV/AIDS, adolescents, sexual minorities, Uganda

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi

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The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.

Keywords: desert soil, climatic changes, bacteria, vegetation, artificial neural networks

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Authors: Zakir Husain

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Main objective of a power transformer design optimization problem requires minimizing the total overall cost and/or mass of the winding and core material by satisfying all possible constraints obligatory by the standards and transformer user requirement. The constraints include appropriate limits on winding fill factor, temperature rise, efficiency, no-load current and voltage regulation. The design optimizations tasks are a constrained minimum cost and/or mass solution by optimally setting the parameters, geometry and require magnetic properties of the transformer. In this paper, present the above design problems have been formulated by using genetic algorithm (GA) and simulated annealing (SA) on the MATLAB platform. The importance of the presented approach is stems for two main features. First, proposed technique provides reliable and efficient solution for the problem of design optimization with several variables. Second, it guaranteed to obtained solution is global optimum. This paper includes a demonstration of the application of the genetic programming GP technique to transformer design.

Keywords: optimization, power transformer, genetic algorithm (GA), simulated annealing technique (SA)

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Authors: Aby Nesan Raj, Sumil K. Raj, Sumesh Jayan

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The objective of the study is to formulate a self-regulation model that shall enhance the usage of Intelligent Transportation Systems by understanding the theory of self-efficacy. The core logic of the self-regulation model shall monitor driver's behavior based on the situations related to the various sources of Self Efficacy like enactive mastery, vicarious experience, verbal persuasion and physiological arousal in addition to the vehicle data. For this study, four different vehicle data, speed, drowsiness, diagnostic data and surround camera views are considered. This data shall be given to the self-regulation model for evaluation. The oddness, which is the output of self-regulation model, shall feed to Intelligent Transportation Systems where appropriate actions are being taken. These actions include warning to the user as well as the input to the related transportation systems. It is also observed that the usage of vehicle as a sensor reduces the wastage of resource utilization or duplication. Altogether, this approach enhances the intelligence of the transportation systems especially in safety, productivity and environmental performance.

Keywords: emergency management, intelligent transportation system, self-efficacy, traffic management

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Authors: Mohd Rashid Bin Hj. Md Idris, Lajiman Bin Janoory, Abdullah Bin Yusof, Mahzir Bin Ibrahim

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The Malay language curriculum at primary school level in Malaysia is instrumental in ensuring the status of the language as the official and national language, the language of instruction as well as the language that unites the various ethnics in Malaysia. A research addressing issues related to the curriculum standard is, therefore, essential to provide value added quality to the existing National Education Philosophy in ongoing efforts to produce an individual who is balanced in intellectual, spiritual, emotional and physical developments. The objective of this study is to examine the Philosophy of Language Theory, to review the content of the Malay language subject in relation to the Standard Curriculum for Primary Schools (KSSR), and to identify aspects of Theory of Philosophy in the Standard Curriculum for Primary Schools. The Malay language Primary School Curriculum is designed to enable students to be competent speakers and communicators of the language in order to gain knowledge, skills, information, values, and ideas and to enhance skills in social relations. Therefore, this study is designed to help educators to achieve all the stated goals. At the same time students at primary school level are expected to be able to apply the principle of language perfection as stated in the Philosophy of Language Theory to enable them to understand, appreciate and to take pride in being a Malaysian who speaks the language well.

Keywords: language, philosophy, theory, curriculum, standard, national education philosophy

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Authors: Abdullah Tahir

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The omission of regular medications in surgical patients poses a significant challenge in healthcare settings and is associated with increased morbidity during hospital stays. Human factors such as high workload, poor communication, and emotional stress are known to contribute to these omissions, particularly evident in the surgical assessment unit (SAU) due to its high patient burden and long wait times. This study aimed to quantify and address the issue by implementing targeted interventions to enhance compliance in prescribing regular medications for surgical patients at Stoke Mandeville Hospital, United Kingdom. Data were collected on 14 spontaneous days between April and May 2023, and the frequency of prescription omissions was recorded using a tally chart. Subsequently, informative posters were introduced in the SAU, and presentations were given to the surgical team to emphasize the importance of compliance in this area. The interventions were assessed using a second data collection cycle, again over 14 spontaneous days in May 2023. Results demonstrated an improvement from 40% (60 out of 150) to 74% (93 out of 126) of patients having regular medications prescribed at the point of clerking. These findings highlight the efficacy of frequent prompts and awareness-raising interventions in increasing workforce compliance and addressing the issue of prescription omissions in the SAU.

Keywords: prescription omissions, quality improvement, regular medication, surgical assessment unit

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Authors: Khansa Hayat

Abstract:

Organizational stress is one of the vital phenomena which is having its roots deep down in has deep roots in management, psychology, and organizational behavior research. In the meanwhile, keeping its focus on the positive strength of humans rather than the traditional negativity oriented research, positive psychology has emerged as a separate branch of organizational behavior. The current study investigates the interactive effects of Challenge and hindrance stressors and core Self Evaluations (CSE’s) of the individual on job performances including the in-role performance and extra role performances. The study also aims to investigate the supporting/buffering role of the human dispositions (i.e., self esteem, self efficacy, locus of control and emotional stability). The results show that Challenge stressors have a significant positive effect on in role performance and extra role performance of the individual. The findings of the study indicate that Core Self evaluations strengthen the relationship between challenge stressors and in role performance of the individual. In case of Hindrance Stressors the Core self Evaluations lessen the negative impact of Hindrance stressors and they let the individual perform at a better and normal position even when the Hindrance stressors are high. The relationship and implication of conservation of resource theory are also discussed. The limitations, future research directions and implications of the study are also discussed.

Keywords: challenge-hindrance stressors, core self evaluations, in-role performance, extra-role performance

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Authors: Nael Y. Zabel, Maged E. Georgy, Moheeb E. Ibrahim

Abstract:

A knowledge-based expert system with the acronym RASPE is developed as an application tool to help decision makers in construction companies make informed decisions about managing risks in pipeline construction projects. Choosing to use expert systems from all available artificial intelligence techniques is due to the fact that an expert system is more suited to representing a domain’s knowledge and the reasoning behind domain-specific decisions. The knowledge-based expert system can capture the knowledge in the form of conditional rules which represent various project scenarios and potential risk mitigation/response actions. The built knowledge in RASPE is utilized through the underlying inference engine that allows the firing of rules relevant to a project scenario into consideration. This paper provides an overview of the knowledge acquisition process and goes about describing the knowledge structure which is divided up into four major modules. The paper shows one module in full detail for illustration purposes and concludes with insightful remarks.

Keywords: expert system, knowledge management, pipeline projects, risk mismanagement

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 256