Search results for: cognitive artificial intelligence

Authors: Saida Haider, Syeda Madiha

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Parkinson’s disease (PD) is a common neurological disorder characterized by motor deficits and loss of dopaminergic neurons. Oxidative stress is said to play a pivotal role in the pathophysiology of the disease. In the present study, PD was induced by injection of rotenone (1.5 mg/kg/day, s.c.) for eight days. Pistachio (800 mg/kg/day, p.o.) was given for two weeks. At the end of treatment brains were dissected out and striatum was isolated for biochemical and neurochemical analysis. Morris water maze (MWM) test and novel object recognition (NOR) task was used to test the memory function while motor behavior was determined by open field test (OFT), Kondziela inverted screen test (KIST), pole test (PT), beam walking test (BWT), inclined plane test (IPT) and footprint (FP) test. Several dietary components have been evaluated as potential therapeutic compounds in many neurodegenerative diseases. Increasing evidence shows that nuts have protective effects against various diseases by improving the oxidative status and reducing lipid peroxidation. Pistachio is the only nut that contains anthocyanin, a potent antioxidant having neuroprotective properties. Results showed that pistachio supplementation significantly restored the rotenone-induced motor deficits and improved the memory performance. Moreover, rats treated with pistachio also exhibited enhanced oxidative status and increased dopamine (DA) and 5-hydroxytryptamine (5-HT) concentration in striatum. In conclusion, to our best knowledge, we have for the first time shown that pistachio nut possesses neuroprotective effects against rotenone-induced motor and cognitive deficits. These beneficial effects of pistachio may be attributed to its high content of natural antioxidant and phenolic compounds. Hence, consumption of pistachio regularly as part of a daily diet can be beneficial in the prevention and treatment of PD.

Keywords: rotenone, pistachio, oxidative stress, Parkinson’s disease

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Authors: Vaikunthan Rajaratnam

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Intro: Motor imagery (MI) and mental practice (MP) can be an alternative to acquire mastery of surgical skills. One component of using this technique is the use of a mental script. The aim of this study was to design and develop a mental script for basic micro suturing training for skill acquisition using a low-fidelity rubber glove model and to describe the detailed methodology for this process. Methods: This study was based on a design and development research framework. The mental script was developed with 5 expert surgeons performing a cognitive walkthrough of the repair of a vertical opening in a rubber glove model using 8/0 nylon. This was followed by a hierarchal task analysis. A draft script was created, and face and content validity assessed with a checking-back process. The final script was validated with the recruitment of 28 participants, assessed using the Mental Imagery Questionnaire (MIQ). Results: The creation of the mental script is detailed in the full text. After assessment by the expert panel, the mental script had good face and content validity. The average overall MIQ score was 5.2 ± 1.1, demonstrating the validity of generating mental imagery from the mental script developed in this study for micro suturing in the rubber glove model. Conclusion: The methodological approach described in this study is based on an instructional design framework to teach surgical skills. This MP model is inexpensive and easily accessible, addressing the challenge of reduced opportunities to practice surgical skills. However, while motor skills are important, other non-technical expertise required by the surgeon is not addressed with this model. Thus, this model should act a surgical training augment, but not replace it.

Keywords: mental script, motor imagery, cognitive walkthrough, verbal protocol analysis, hierarchical task analysis

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Authors: Ivan Kuzio

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The GIZ/TTC project is the first of its kind in the Middle East, which allows the development of a teaching training programme to degree level based on modern methodologies. The graduates from this college are part of the Saudization programme and will, over the next four years be part of and eventually run the new Colleges of Excellence. The new Colleges of Excellence are being developed to create a local vocationally trained workforce and will run initially alongside the current Colleges of Technology.

Keywords: blended learning, pedagogy, training, key competencies, social skills, cognitive development

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Authors: Célia Barreto Carvalho, Carolina da Motta, José Pinto-Gouveia, Ermelindo Bernardo Peixoto

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Auditory hallucinations among the most invalidating and distressing experiences reported by patients diagnosed with schizophrenia, leading to feelings of powerlessness and helplessness towards their illness. In more severe cases, these auditory hallucinations can take the form of commanding voices, which are often related to high suicidality rates in these patients. Several authors propose that the meanings attributed to the hallucinatory experience, rather than characteristics like form and content, can be determinant in patients’ reactions to hallucinatory activity, particularly in the case of voice-hearing experiences. In this study, 48 patients diagnosed with paranoid schizophrenia presenting auditory hallucinations were studied. Multiple regression analyses were computed to study the influence of several developmental aspects, such as family and social dynamics, bullying, depression, and socio-cognitive variables on the auditory hallucinations, on patients’ attributions and relationships with their voices, and on the resulting invalidation of hallucinatory experience. Overall, results showed how relationships with voices can mirror several aspects of interpersonal relationship with others, and how self-schemas, depression and actual social relationships help shaping the voice-hearing experience. Early experiences of victimization and submission help predict the attributions of omnipotence of the voices, and increased hostility from parents seems to increase the malevolence of the voices, suggesting that socio-cognitive factors can significantly contribute to the etiology and maintenance of auditory hallucinations. The understanding of the characteristics of auditory hallucinations and the relationships patients established with their voices can allow the development of more promising therapeutic interventions that can be more effective in decreasing invalidation caused by this devastating mental illness.

Keywords: auditory hallucination, beliefs, life events, schizophrenia

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Authors: Kanu Priya Mohan

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Innovation in education is one of the essential pathways to achieve both educational, and development goals in today’s dynamically changing world. Over the last decade, coaching and mentoring have been applied in the field of education as positive intervention techniques for fostering teaching and learning reforms in the developed countries. The context of this research was Thailand’s educational reform process, wherein a project on coaching and mentoring (C&M) was launched in 2014. The C&M project endeavored to support the professional development of the school teachers in the various provinces of Thailand, and to also enable them to apply C&M for teaching innovative instructional techniques. This research aimed to empirically investigate the learning outcomes for the master trainers, who trained for coaching and mentoring as the first step in the process to train the school teachers. A mixed method study was used for evaluating the learning outcomes of training in terms of cognitive- behavioral-affective dimensions. In the first part of the research a quantitative research design was incorporated to evaluate the effects of learner characteristics and instructional techniques, on the learning outcomes. In the second phase, a qualitative method of in-depth interviews was used to find details about the training outcomes, as well as the perceived barriers and enablers of the training process. Sample size constraints were there, yet these exploratory results, integrated from both methods indicated the significance of evaluating training outcomes from the three dimensions, and the perceived role of other factors in the training. Findings are discussed in terms of their implications for the training of C&M, and also their impact in fostering positive education through innovative educational techniques in the developing countries.

Keywords: cognitive-behavioral-affective learning outcomes, mixed method research, teachers in Thailand, training evaluation

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Authors: Lavrentiadou S. N., Angelou V., Chatzimisios K., Papazoglou L.

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The aim of this study was the isolation and culture of keratinocytes and fibroblasts from feline skin to ultimately create an artificial engineered skin (including dermis and epidermis) useful for the effective treatment of large cutaneous deficits in cats. Epidermal keratinocytes and dermal fibroblasts were freshly isolated from skin biopsies using an 8 mm biopsy punch obtained from 8 healthy cats that had undergone ovariohysterectomy. The owner’s consent was obtained. All cats had a complete blood count and a serum biochemical analysis and were screened for feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV) preoperatively. The samples were cut into small pieces and incubated with collagenase (2 mg/ml) for 5-6 hours. Following digestion, cutaneous cells were filtered through a 100 μm cell strainer, washed with DMEM, and grown in DMEM supplemented with 10% FBS. The undigested epidermis was washed with DMEM and incubated with 0.05% Trypsin/0.02% EDTA (TE) solution. Keratinocytes recovered in the TE solution were filtered through a 100 μm and a 40 μm cell strainer and, following washing, were grown on a collagen type I matrix in DMEM: F12 (3:1) medium supplemented with 10% FΒS, 1 μm hydrocortisone, 1 μm isoproterenol and 0.1 μm insulin. Both fibroblasts and keratinocytes were grown in a humidified atmosphere with 5% CO2 at 37oC. The medium was changed twice a week and cells were cultured up to passage 4. Cells were grown to 70-85% confluency, at which point they were trypsinized and subcultured in a 1:4 dilution. The majority of the cells in each passage were transferred to a freezing medium and stored at -80oC. Fibroblasts were frozen in DMEM supplemented with 30% FBS and 10% DMSO, whereas keratinocytes were frozen in a complete keratinocyte growth medium supplemented with 10% DMSO. Both cell types were thawed and successfully grown as described above. Therefore, we can create a bank of fibroblasts and keratinocytes, from which we can recover cells for further culture and use for the generation of skin equivalent in vitro. In conclusion, cutaneous cell isolation and cell culture and expansion were successfully developed. To the authors’ best knowledge, this is the first study reporting isolation and culture of keratinocytes and fibroblasts from feline skin. However, these are preliminary results and thus, the development of autologous-engineered feline skin is still in process.

Keywords: cat, fibroblasts, keratinocytes, skin equivalent, wound

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Authors: Juan Carlos Rubio Romero, Francico Salguero Caparros, Virginia Herrera-Pérez

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No one is in the slightest doubt about the high levels of risk involved in work in the construction industry. They are even higher in structural construction work. The Critical Decision Method (CDM) is a semi-structured interview technique that uses cognitive tests to identify the different disturbances that workers have to deal with in their work activity. At present, the vision of safety focused on daily performance and things that go well for safety and health management is facing the new paradigm known as Resilience Engineering. The aim of this study has been to describe the variability in formwork labour on concrete structures in the construction industry and, from there, to find out the resilient attitude of workers to unexpected events that they have experienced during their working lives. For this purpose, a series of semi-structured interviews were carried out with construction employees with extensive experience in formwork labour in Spain by applying the Critical Decision Method. This work has been the first application of the Critical Decision Method in the field of construction and, more specifically, in the execution of structures. The results obtained show that situations categorised as unthought-of are identified to a greater extent than potentially unexpected situations. The identification during these interviews of both expected and unexpected events provides insight into the critical decisions made and actions taken to improve resilience in daily practice in this construction work. From this study, it is clear that it is essential to gain more knowledge about the nature of the human cognitive process in work situations within complex socio-technical systems such as construction sites. This could lead to a more effective design of workplaces in the search for improved human performance.

Keywords: resilience engineering, construction industry, unthought-of situations, critical decision method

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Authors: Yoon Suh Song

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Though music education can broaden one’s capacity for mathematical performance, many countries lag behind in music education. Little empirical evidence is found to identify the connection between math and music. Therefore, this research was set out to explore what music-related variables are associated with mathematical performance. The result of our analysis is as follows: A Pearson's Correlation analysis revealed that PISA math score is strongly correlated with students' Intelligence Quotient (IQ). This lays the foundation for further research as to what factors in students’ IQ lead to a better performance in math.

Keywords: music education, mathematical performance, education, IQ

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Authors: A. M. Khourshid, I. Sabry

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Friction Stir Welding (FSW), a solid state joining technique, is widely being used for joining Al alloys for aerospace, marine automotive and many other applications of commercial importance. FSW were carried out using a vertical milling machine on Al 5083 alloy pipe. These pipe sections are relatively small in diameter, 5mm, and relatively thin walled, 2 mm. In this study, 5083 aluminum alloy pipe were welded as similar alloy joints using (FSW) process in order to investigate mechanical and microstructural properties .rotation speed 1400 r.p.m and weld speed 10,40,70 mm/min. In order to investigate the effect of welding speeds on mechanical properties, metallographic and mechanical tests were carried out on the welded areas. Vickers hardness profile and tensile tests of the joints as a metallurgical feasibility of friction stir welding for joining Al 6061 aluminum alloy welding was performed on pipe with different thickness 2, 3 and 4 mm,five rotational speeds (485,710,910,1120 and 1400) rpm and a traverse speed (4, 8 and 10)mm/min was applied. This work focuses on two methods such as artificial neural networks using software (pythia) and response surface methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminum alloy. An artificial neural network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. The tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters Tool rotation speed, material thickness and travel speed as a function. A comparison was made between measured and predicted data. Response surface methodology (RSM) also developed and the values obtained for the response Tensile strengths, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameter on mechanical properties of 6061 aluminum alloy has been analyzed in detail.

Keywords: friction stir welding (FSW), al alloys, mechanical properties, microstructure

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Authors: Roberto Cabezas H

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The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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Authors: Wichai Srisud, Shunnawat Pungbangkradee, Sukanya Chaemchoy

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This research study aims to analyze and assess school management guidelines designed to enhance the level of Student School Engagement of students at Bangkok Christian College, according to three following primary objectives: 1) to evaluate the level of Student School Engagement among Bangkok Christian College students, 2) to examine the Priority Needs Index of school management for promoting an optimum level of Student School Engagement among Bangkok Christian College students, and 3) to develop additional guidelines for school management to further enhance the level of Student School Engagement of Bangkok Christian College students. The research was conducted using Explanatory Design research methodology, with data obtained from a sample comprised of 291 students and 6 administrative personnel. The research findings indicated that: 1) The overall level of Student School Engagement was high. Emotional engagement averaged at the highest level, followed by Behavioral Engagement and Cognitive Engagement, respectively. 2) The Priority Needs Index of school management for promoting Student School Engagement of Bangkok Christian College students was examined, revealing that Evaluation averaged at the highest PNI level, followed by Planning and Implementation, respectively. 3) Guidelines for school management to enhance Student School Engagement of Bangkok Christian College students should consist of four approaches: 3.1) A Cognitive Engagement Enhancing Approach, which must include (1) fostering students’ problem-solving flexibility, and their ability to devise solutions for overcoming potential challenges, and (2) encouraging students to deal effectively with academic setbacks, rather than becoming overwhelmed by what they may perceive as failures, 3.2) An Emotional Engagement Enhancing Approach, cultivating students’ interests, aspirations and goals in learning to maximize emotional investment in their academic pursuits, and 3.3) A Behavioral Engagement Enhancing Approach, for elevating students’ focus and attentiveness during learning, and improving their ability to avoid distractions during study time.

Keywords: school engagement, guidelines for school management

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Authors: C. Slim

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This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial Neural Networks (ANN), Support vector machine (SVM), Data Envelopment Analysis (DEA), Aggregations, indicators of performance

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Authors: John E. Boberg, Steven J. Bourgeois

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The decline in student engagement and motivation through the middle years is well documented and clearly associated with a decline in mathematics achievement that persists through high school. To combat this trend and, very often, to meet high-stakes accountability standards, a growing number of parents, teachers, and schools have implemented various methods to incentivize learning. However, according to Self-Determination Theory, forms of incentivized learning such as public praise, tangible rewards, or threats of punishment tend to undermine intrinsic motivation and learning. By focusing on external forms of motivation that thwart autonomy in children, adults also potentially threaten relatedness measures such as trust and emotional engagement. Furthermore, these controlling motivational techniques tend to promote shallow forms of cognitive engagement at the expense of more effective deep processing strategies. Therefore, any short-term gains in apparent engagement or test scores are overshadowed by long-term diminished motivation, resulting in inauthentic approaches to learning and lower achievement. The current study focuses on the relationships between student trust, engagement, and motivation during these crucial years as students transition from elementary to middle school. In order to test the effects of controlled motivational techniques on achievement in mathematics, this quantitative study was conducted on a convenience sample of 22 elementary and middle schools from a single public charter school district in the south-central United States. The study employed multi-source data from students (N = 1,054), parents (N = 7,166), and teachers (N = 356), along with student achievement data and contextual campus variables. Cross-sectional questionnaires were used to measure the students’ self-regulated learning, emotional and cognitive engagement, and trust in teachers. Parents responded to a single item on incentivizing the academic performance of their child, and teachers responded to a series of questions about their acceptance of various incentive strategies. Structural equation modeling (SEM) was used to evaluate model fit and analyze the direct and indirect effects of the predictor variables on achievement. Although a student’s trust in teacher positively predicted both emotional and cognitive engagement, none of these three predictors accounted for any variance in achievement in mathematics. The parents’ use of incentives, on the other hand, predicted a student’s perception of his or her controlled motivation, and these two variables had significant negative effects on achievement. While controlled motivation had the greatest effects on achievement, parental incentives demonstrated both direct and indirect effects on achievement through the students’ self-reported controlled motivation. Comparing upper elementary student data with middle-school student data revealed that controlling forms of motivation may be taking their toll on student trust and engagement over time. While parental incentives positively predicted both cognitive and emotional engagement in the younger sub-group, such forms of controlling motivation negatively predicted both trust in teachers and emotional engagement in the middle-school sub-group. These findings support the claims, posited by Self-Determination Theory, about the dangers of incentivizing learning. Short-term gains belie the underlying damage to motivational processes that lead to decreased intrinsic motivation and achievement. Such practices also appear to thwart basic human needs such as relatedness.

Keywords: controlled motivation, student engagement, incentivized learning, mathematics achievement, self-determination theory, student trust

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Ankit Shah, R. K. Shrivastava

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

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

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Authors: Jolanta Jonak

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Traditional classrooms are challenging for many students, but especially for students that learn differently due to cognitive makeup, learning preferences, or disability. These students often require different teaching approaches and learning opportunities to benefit from learning. Teachers frequently divert to using one teaching approach, the one that matches their own learning style. For instance, teachers that are auditory learners, likely default to providing auditory learning opportunities. However, if a student is a visual learner, he/she may not fully benefit from that teaching style. Based on research, students and their parents’ feedback, large numbers of students are not provided the type of education and types of supports they need in order to be successful in an academic environment. This eventually leads to not learning at an appropriate rate and ultimately leading to skill deficiencies and deficits. Providing varied learning approaches promote high academic and social-emotional growth of all students and it will prevent inaccurate Special Education referrals. Varied learning opportunities can be delivered for all students by providing Differentiated Instruction (DI). This type of instruction allows each student to learn in the most optimal way regardless of learning preferences and cognitive learning profiles. Using Differentiated Instruction will lead to a high level of student engagement and learning. In addition, experiencing success in the classroom, will contribute to increased social emotional wellbeing. Being cognizant of how teaching approaches impact student's learning, school staff can avoid inaccurate perceptions about the students’ learning abilities, unnecessary referrals for special education evaluations, and inaccurate decisions about the presence of a disability. This presentation will illustrate learning differences due to various factors, how to recognize them, and how to address them through Differentiated Instruction.

Keywords: special education, disability, differences, differentiated instruction, social emotional wellbeing

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Mohammed Mainoma, Abubakar Tijanni, Mohammed Aliyu

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Globalization has brought a structural change in industry. It is the breaking of artificial boundaries and given way to new product, new service, new market, and new technology among others. It leads to the realization that men entrepreneurs’ alone cannot meet the demand of the teeming population. Therefore there is a need for the participation, involvement, and engagement of females in the production and distribution of goods and services. This will enhance growth and development of a nation. It is in line with the above that this paper attempt to discuss meaning of women entrepreneurs, roles, types, problems, and prospects. Also, on the basis of conclusion the paper recommended that entrepreneurship education should be introduced in all Tertiary Institutions in Nigeria.

Keywords: women, entrepreneurs, issues, challenges

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Authors: Mamunur Rashid, Jialin Yan

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The current years have witnessed a rapid evolution and integration of artificial intelligence (AI) in various fields, prominently influencing the education industry. Acknowledging this transformative wave, AI tools like ChatGPT and Grammarly have undeniably introduced perspectives and skills, enriching the educational experiences of higher education students. The prevalence of AI utilization in higher education also drives an increasing number of researchers' attention in various dimensions. Departments, offices, and professors in universities also designed and released a set of policies and guidelines on using AI effectively. In regard to this, the study targets exploring and analyzing graduate students' perspectives regarding AI guidelines set by teachers. A mixed-methods study will be mainly conducted in this study, employing in-depth interviews and focus groups to investigate and collect students' perspectives. Relevant materials, such as syllabi and course instructions, will also be analyzed through the documentary analysis to facilitate understanding of the study. Surveys will also be used for data collection and students' background statistics. The integration of both interviews and surveys will provide a comprehensive array of student perspectives across various academic disciplines. The study is anchored in the theoretical framework of self-determination theory (SDT), which emphasizes and explains the students' perspective under the AI guidelines through three core needs: autonomy, competence, and relatedness. This framework is instrumental in understanding how AI guidelines influence students' intrinsic motivation and sense of empowerment in their learning environments. Through qualitative analysis, the study reveals a sense of confusion and uncertainty among students regarding the appropriate application and ethical considerations of AI tools, indicating potential challenges in meeting their needs for competence and autonomy. The quantitative data further elucidates these findings, highlighting a significant communication gap between students and educators in the formulation and implementation of AI guidelines. The critical findings of this study mainly come from two aspects: First, the majority of graduate students are uncertain and confused about relevant AI guidelines given by teachers. Second, this study also demonstrates that the design and effectiveness of course materials, such as the syllabi and instructions, also need to adapt in regard to AI policies. It indicates that certain of the existing guidelines provided by teachers lack consideration of students' perspectives, leading to a misalignment with students' needs for autonomy, competence, and relatedness. More emphasize and efforts need to be dedicated to both teacher and student training on AI policies and ethical considerations. To conclude, in this study, graduate students' perspectives on teacher-provided AI guidelines are explored and reflected upon, calling for additional training and strategies to improve how these guidelines can be better disseminated for their effective integration and adoption. Although AI guidelines provided by teachers may be helpful and provide new insights for students, educational institutions should take a more anchoring role to foster a motivating, empowering, and student-centered learning environment. The study also provides some relevant recommendations, including guidance for students on the ethical use of AI and AI policy training for teachers in higher education.

Keywords: higher education policy, graduate students’ perspectives, higher education teacher, AI guidelines, AI in education

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Authors: dr. Bendes Ákos

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Digital skepticism, a critical stance towards digital technology and its pervasive influence on society, presents significant challenges when analyzed from a legal philosophical perspective. This abstract aims to explore the intersection of digital skepticism and legal philosophy, emphasizing the implications for justice, rights, and the rule of law in the digital age. Digital skepticism arises from concerns about privacy, security, and the ethical implications of digital technology. It questions the extent to which digital advancements enhance or undermine fundamental human values. Legal philosophy, which interrogates the foundations and purposes of law, provides a framework for examining these concerns critically. One key area where digital skepticism and legal philosophy intersect is in the realm of privacy. Digital technologies, particularly data collection and surveillance mechanisms, pose substantial threats to individual privacy. Legal philosophers must grapple with questions about the limits of state power and the protection of personal autonomy. They must consider how traditional legal principles, such as the right to privacy, can be adapted or reinterpreted in light of new technological realities. Security is another critical concern. Digital skepticism highlights vulnerabilities in cybersecurity and the potential for malicious activities, such as hacking and cybercrime, to disrupt legal systems and societal order. Legal philosophy must address how laws can evolve to protect against these new forms of threats while balancing security with civil liberties. Ethics plays a central role in this discourse. Digital technologies raise ethical dilemmas, such as the development and use of artificial intelligence and machine learning algorithms that may perpetuate biases or make decisions without human oversight. Legal philosophers must evaluate the moral responsibilities of those who design and implement these technologies and consider the implications for justice and fairness. Furthermore, digital skepticism prompts a reevaluation of the concept of the rule of law. In an increasingly digital world, maintaining transparency, accountability, and fairness becomes more complex. Legal philosophers must explore how legal frameworks can ensure that digital technologies serve the public good and do not entrench power imbalances or erode democratic principles. Finally, the intersection of digital skepticism and legal philosophy has practical implications for policy-making. Legal scholars and practitioners must work collaboratively to develop regulations and guidelines that address the challenges posed by digital technology. This includes crafting laws that protect individual rights, ensure security, and promote ethical standards in technology development and deployment. In conclusion, digital skepticism provides a crucial lens for examining the impact of digital technology on law and society. A legal philosophical approach offers valuable insights into how legal systems can adapt to protect fundamental values in the digital age. By addressing privacy, security, ethics, and the rule of law, legal philosophers can help shape a future where digital advancements enhance, rather than undermine, justice and human dignity.

Keywords: legal philosophy, privacy, security, ethics, digital skepticism

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Authors: David Nchinda Keming

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The present study is focused on both the national and international collective fight against Boko Haram terrorism on Cameroon and the rule played by the Lake Chad Basin Countries (LCBCs) and the global community to suffocate the sect’s activities in the region. Although countries of the Lake Chad Basin include: Cameroon, Chad, Nigeria and Niger others like Benin also joined the course. The justification for the internationalisation of the fight against Boko Haram could be explained by the ecological and international climatic importance of the Lake Chad and the danger posed by the sect not only to the Lake Chad member countries but to global armed, civil servants and the international political economy. The study, therefore, kick start with Cameroon’s reaction to Boko Haram’s terrorist attacks on its territory. It further expounds on Cameroon’s request on bilateral diplomacy from members of the UN Security Council for an international collective support to staple the winds of the challenging sect. The study relies on the hypothesis that Boko Haram advanced terrorism on Cameroon was more challenging to the domestic military intelligence thus forcing the government to seek for bilateral and multilateral international collective support to secure its territory from the powerful sect. This premise is tested internationally via (multilateral cooperation, bilateral response, regional cooperation) and domestically through (solidarity parade, religious discourse, political manifestations, war efforts, the vigilantes and the way forward). To accomplish our study, we made used of the mixed research methodologies to interpret the primary, secondary and tertiary sources consulted. Our results reveal that the collective response was effectively positive justified by the drastic drop in the sect’s operations in Cameroon and the whole LCBCs. Although the sect was incapacitated, terrorism remains an international malaise and Cameroon hosts a fertile ground for terrorists’ activism. Boko Haram was just weakened and not completely defeated and could reappear someday even under a different appellation. Therefore, to absolutely eradicate terrorism in general and Boko Haram in particular, LCBCs must improve their military intelligence on terrorism and continue to collaborate with advanced experienced countries in fighting terrorism.

Keywords: Boko Haram, terrorism, domestic, international, response

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Authors: Raghavi Janaswamy, Saraswathi K. Vasudev

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Music is ubiquitous in human lives. Ever since the fetus hears the sound inside the mother’s womb and later upon birth, the baby experiences alluring sounds, the curiosity of learning emanates and evokes exploration. Music is an education than mere entertainment. The intricate balance between music, education, and entertainment has well been recognized by the scientific community and is being explored as a viable tool to understand and improve human cognition. There are seven basic swaras (notes) Sa, Ri, Ga, Ma, Pa, Da, and Ni in the Carnatic music system that are analogous to C, D, E, F, G, A, and B of the western system. The Carnatic music builds on the conscious use of microtones, gamakams (oscillation), and rendering styles that evolved over centuries and established its stance. The complex but erudite raga system has been designed with elaborate experiments on srutis (musical sounds) and human perception abilities. In parallel, ‘rasa’- the emotions evoked by certain srutis and hence the ragas been solidified along with the power of language in combination with the musical sounds. The Carnatic music branches out as Kalpita sangeetam (pre-composed music) and Manodharma sangeetam (improvised music). This article explores the Manodharma sangeetam and its subdivisions such as raga alapana, swara kalpana, neraval, and ragam-tanam-pallavi (RTP). The intrinsic mathematical strategies in it’s practice methods toward improvising the music have been explored in detail with concert examples. The techniques on swara weaving for swara kalpana rendering and methods on the alapana development are also discussed at length with an emphasis on the impact on the human cognitive abilities. The articulation of the outlined conscious practice methods not only helps to leave a long-lasting melodic impression on the listeners but also onsets cognitive developments.

Keywords: Carnatic, Manodharmam, music cognition, Alapana

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Authors: Ayşe Defne Öz, Özlem Bozkurt

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Alzheimer's Disease (AD) is counted as one of the most important global health problems and the main cause of dementia. The term dementia refers to a wide spectrum of disorders characterized by global, chronic, and generally irreversible cognitive deterioration. It is estimated that %60 % to 80 of the cases of dementia are because of AD. Alzheimer's is a slowly progressive brain disease. The reason for AD is unknown to the author's best knowledge, yet it is one of the topics that is most researched. AD shows the histopathologically abnormal accumulation of the protein beta-amyloid (plague) outside neurons and twisted strands of the protein tau (tangles) inside neurons in the brain. These changes are accompanied by damage to the brain tissue and the death of neurons. AD causes people to have difficulty remembering names or conversations. Some of the later symptoms are difficulty in talking and walking. Alzheimer's Disease is elevated by the illness and mortality of COVID-19. COVID-19 has affected many lives globally and had profound effects on human lives. COVID-19 is caused by SARS-CoV-2, which is a virus that attacks the respiratory and central nervous system and has neuroinvasive potential. More than %80 of COVID-19 patients have ageusia or anosmia, representing the pathognomic features of the disease. Patients with dementia are frail, and with the COVID-19 pandemic, including isolation, cognitive decline may exacerbate. Furthermore, patients with AD can be unable to follow the directions, such as covering their mouth and nose while coughing and can live in nursing homes which makes them more open to being infected. As COVID-19 is highly infectious and its management requires isolation and quarantine, the need for caregivers for AD management conflicts with that of COVID-19 and adds an extra burden on AD patients, caregivers, families, society, and the economy. Due to the entry of SARS-CoV-2 into the central nervous system, inflammation caused by COVID-19, prolonged hospitalization, and delirium, it has been reported that COVID-19 causes many neurological disorders and predisposition to AD.

Keywords: Alzheimer's disease, COVID-19, dementia, SARS-CoV-2

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Authors: Ranjeet Kumar, Pravin Suryakantrao Deshmukh, Sonal Sharma, Basudev Banerjee

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With the tremendous increase in exposure to radiofrequency microwaves emitted by mobile phones, globally public awareness has grown with regard to the potential health hazards of microwaves on the nervous system in the brain. India alone has more than one billion mobile users out of 4.3 billion globally. Our studies have suggested that radio frequency able to affect neuronal alterations in the brain, and hence, affecting cognitive behaviour. However, adverse effect of low-intensity microwave exposure with endoplasmic reticulum stress and autophagy has not been evaluated yet. In this study, we explore whether low-intensity microwave induces endoplasmic reticulum stress and autophagy with varying frequency and time duration in Wistar rat. Ninety-six male Wistar rat were divided into 12 groups of 8 rats each. We studied at 900 MHz, 1800 MHz, and 2450 MHz frequency with reference to sham-exposed group. At the end of the exposure, the rats were sacrificed to collect brain tissue and expression of CHOP, ATF-4, XBP-1, Bcl-2, Bax, LC3 and Atg-4 gene was analysed by real-time PCR. Significant fold change (p < 0.05) of gene expression was found in all groups of 1800 MHz and 2450 MHz exposure group in comparison to sham exposure group. In conclusion, the microwave exposure able to induce ER stress and modulate autophagy. ER (endoplasmic reticulum) stress and autophagy vary with increasing frequency as well as the duration of exposure. Our results suggested that microwave exposure is harmful to neuronal health as it induces ER stress and hampers autophagy in neuron cells and thereby increasing the neuron degeneration which impairs cognitive behaviour of experimental animals.

Keywords: autophagy, ER stress, microwave, nervous system, rat

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Authors: S. Pesina, T. Solonchak

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Lexical invariants, being a sort of stereotypes within the frames of ordinary consciousness, are created by the members of a language community as a result of uniform division of reality. The invariant meaning is formed in person’s mind gradually in the course of different actualizations of secondary meanings in various contexts. We understand lexical the invariant as abstract language essence containing a set of semantic components. In one of its configurations it is the basis or all or a number of the meanings making up the semantic structure of the word.

Keywords: lexical invariant, invariant theories, polysemantic word, cognitive linguistics

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Authors: Claire Tranter, Coral Dando

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Gathering information from others is a fundamental goal for those concerned with investigating crime, and protecting national and international security. Persuading an individual to move from an opposing to converging viewpoint, and an understanding on the cognitive style behind this change can serve to increase understanding of traditional face-to-face interactions, as well as synthetic environments (SEs) often used for communication across varying geographical locations. SEs are growing in usage, and with this increase comes an increase in crime being undertaken online. Communication technologies can allow people to mask their real identities, supporting anonymous communication which can raise significant challenges for investigators when monitoring and managing these conversations inside SEs. To date, the psychological literature concerning how to maximise information-gain in SEs for real-world interviewing purposes is sparse, and as such this aspect of social cognition is not well understood. Here, we introduce an overview of a novel programme of PhD research which seeks to enhance understanding of cross-cultural and cross-gender communication in SEs for maximising information gain. Utilising a dyadic jury paradigm, participants interacted with a confederate who attempted to persuade them to the opposing verdict across three distinct environments: face-to-face, instant messaging, and a novel virtual reality environment utilising avatars. Participants discussed a criminal scenario, acting as a two-person (male; female) jury. Persuasion was manipulated by the confederate claiming an opposing viewpoint (guilty v. not guilty) to the naïve participants from the outset. Pre and post discussion data, and observational digital recordings (voice and video) of participant’ discussion performance was collected. Information regarding cognitive style was also collected to ascertain participants need for cognitive closure and biases towards jumping to conclusions. Findings revealed that individuals communicating via an avatar in a virtual reality environment reacted in a similar way, and thus equally persuasive, when compared to individuals communicating face-to-face. Anonymous instant messaging however created a resistance to persuasion in participants, with males showing a significant decline in persuasive outcomes compared to face to face. The findings reveal new insights particularly regarding the interplay of persuasion on gender and modality, with anonymous instant messaging enhancing resistance to persuasion attempts. This study illuminates how varying SE can support new theoretical and applied understandings of how judgments are formed and modified in response to advocacy.

Keywords: applied cognition, persuasion, social media, virtual reality

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Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

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Authors: Isomar Lima da Silva, Alcilene Batalha Pontes, Aristeu Jonatas Leite de Oliveira, Roberto Maia Augusto

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Context: Mechanical ventilators are medical devices that help provide oxygen and ventilation to patients with respiratory difficulties. This equipment consists of a manual breathing unit that can be operated by a doctor or nurse and a mechanical ventilator that controls the airflow and pressure in the patient's respiratory system. This type of ventilator is commonly used in emergencies and intensive care units where it is necessary to provide breathing support to critically ill or injured patients. Objective: In this context, this work aims to develop a reliable and low-cost mechanical ventilator to meet the demand of hospitals in treating people affected by Covid-19 and other severe respiratory diseases, offering a chance of treatment as an alternative to mechanical ventilators currently available in the market. Method: The project presents the development of a low-cost auxiliary ventilator with a controlled ventilatory system assisted by integrated hardware and firmware for respiratory cycle control in non-invasive mechanical ventilation treatments using a manual artificial respiration unit. The hardware includes pressure sensors capable of identifying positive expiratory pressure, peak inspiratory flow, and injected air volume. The embedded system controls the data sent by the sensors. It ensures efficient patient breathing through the operation of the sensors, microcontroller, and actuator, providing patient data information to the healthcare professional (system operator) through the graphical interface and enabling clinical parameter adjustments as needed. Results: The test data of the developed mechanical ventilator presented satisfactory results in terms of performance and reliability, showing that the equipment developed can be a viable alternative to commercial mechanical ventilators currently available, offering a low-cost solution to meet the increasing demand for respiratory support equipment.

Keywords: mechanical fans, breathing, medical equipment, COVID-19, intensive care units

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Authors: Haris Karnezi, Kevin Tierney

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Cognitive Behaviour Drama is a research-based intervention model that brought together the science of psychology with the art form of drama to create an unobtrusive and exciting approach that would provide children on the higher end of the autism spectrum the motivation to explore the rules of social interaction and develop competencies associated with communicative success. The method involves engaging the participants in exciting fictional scenarios and encouraging them to seek various solutions on a number of problems that will lead them to an understanding of causal relationships and how a different course of action may lead to a different outcome. The sessions are structured to offer opportunities to the participants to practice target behaviours and understand the functions they serve. The study involved six separate interventions and employed both single case and group designs. Overall 8 children aged between 6 to 13 years, diagnosed with ASD participated in the study. Outcomes were measured using theory of mind tests, executive functioning tests, behavioural observations, pre and post intervention standardised social competence questionnaires for parents and teachers. Collectively, the results indicated positive changes in the self esteem and behaviour of all eight participants. In particular, improvements in the ability to solve theory of mind tasks were noted in the younger group; and qualitative improvements in social communication, in terms of verbal (content) and non verbal expression (body posture, vocal expression, fluency, eye contact, reduction of ritualistic mannerisms) were noted in the older group. The need for reliable impact measures to assess the effectiveness of the model in generating global changes in the participants’ behaviour outside the therapeutic context was identified.

Keywords: autism, drama, intervention, social skills

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Authors: Paria Tajabor, Azin Azarbani

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The purpose of this research study is exploring two kind of semantic repositories with regards to various factors to find the best approaches that an artificial manager can use to produce ontology in a system based on their interaction, association and research. To this end, as the best way to evaluate each system and comparing with others is analysis, several benchmarking over these two repositories were examined. These two semantic repositories: OWLIM and AllegroGraph will be the main core of this study. The general objective of this study is to be able to create an efficient and cost-effective manner reports which is required to support decision making in any large enterprise.

Keywords: OWLIM, allegrograph, RDF, reasoning, semantic repository, semantic-web, SPARQL, ontology, query

Procedia PDF Downloads 249