Search results for: cognitive intelligence

Authors: Adam J. Bowen

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Abstract— Should robots have rights or legal protections. Often debates concerning whether robots and AI should be afforded rights focus on conditions of personhood and the possibility of future advanced forms of AI satisfying particular intrinsic cognitive and moral attributes of rights-holding persons. Such discussions raise compelling questions about machine consciousness, autonomy, and value alignment with human interests. Although these are important theoretical concerns, especially from a future design perspective, they provide limited guidance for addressing the moral and legal standing of current and near-term AI that operate well below the cognitive and moral agency of human persons. Robots and AI are already being pressed into service in a wide range of roles, especially in healthcare and biomedical contexts. The design and large-scale implementation of robots in the context of core societal institutions like healthcare systems continues to rapidly develop. For example, we bring them into our homes, hospitals, and other care facilities to assist in care for the sick, disabled, elderly, children, or otherwise vulnerable persons. We enlist surgical robotic systems in precision tasks, albeit still human-in-the-loop technology controlled by surgeons. We also entrust them with social roles involving companionship and even assisting in intimate caregiving tasks (e.g., bathing, feeding, turning, medicine administration, monitoring, transporting). There have been advances to enable severely disabled persons to use robots to feed themselves or pilot robot avatars to work in service industries. As the applications for near-term AI increase and the roles of robots in restructuring our biomedical practices expand, we face pressing questions about the normative implications of human-robot interactions and collaborations in our collective worldmaking, as well as the moral and legal status of robots. This paper argues that robots operating in public and private spaces be afforded some protections as either moral patients or legal agents to establish prohibitions on robot abuse, misuse, and mistreatment. We already implement robots and embed them in our practices and institutions, which generates a host of human-to-machine and machine-to-machine relationships. As we interact with machines, whether in service contexts, medical assistance, or home health companions, these robots are first encountered in relationship to us and our respective roles in the encounter (e.g., surgeon, physical or occupational therapist, recipient of care, patient’s family, healthcare professional, stakeholder). This proposal aims to outline a framework for establishing limiting factors and determining the extent of moral or legal protections for robots. In doing so, it advocates for a relational approach that emphasizes the priority of mapping the complex contextually sensitive roles played and the relations in which humans and robots stand to guide policy determinations by relevant institutions and authorities. The relational approach must also be technically informed by the intended uses of the biomedical technologies in question, Design History Files, extensive risk assessments and hazard analyses, as well as use case social impact assessments.

Keywords: biomedical robots, robot ethics, robot laws, human-robot interaction

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Authors: Roba Zakariah Shaheen

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In the 21st-century Interior, design pedagogy has developed rapidly due to social and economical factors. Socially, this paper presents research findings that shows a significant relationship between educators and students in interior design education. It shows that students’ personal traits, design process, and thinking process are significantly interrelated. Constructively, this paper presented how personal traits can guide educators in the interior design education domain to develop students’ thinking process. In the same time, it demonstrated how students should use their own personal traits to create their own design process. Constructivism was the theory underneath this research, as it supports the grounded theory, which is the methodological approach of this research. Moreover, Mayer’s Briggs Type Indicator strategy was used to investigate the personality traits scientifically, as a psychological strategy that related to cognitive ability. Conclusions from this research strongly recommends that educators and students should utilize their personal traits to foster interior design education.

Keywords: interior design, pedagogy, constructivism, grounded theory, personality traits, creativity

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Authors: Morsli Mourad, Tahiri Mohamed, Samdi Azzeddine

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Casablanca is the biggest city in Morocco. It concentrates more than 60% of the economic and industrial activity of the kingdom. Its building and public works (BTP) sector is the leading source of inert waste scattered in open areas. This inert waste is a major challenge for the city of Casablanca, as it is not properly managed, thus causing a significant nuisance for the environment and the health of the population. Hence the vision of our project is to recycle and valorize concrete waste. In this work, we present concrete results in the exploitation of this abundant and permanent deposit. Typical wastes are concrete, clay and concrete bricks, ceramic tiles, marble panels, gypsum, scrap metal, wood . The work performed included: geolocation with a combination of artificial intelligence and Google Earth, estimation of the amount of waste per site, sorting, crushing, grinding, and physicochemical characterization of the samples. Then, we proceeded to the exploitation of the types of substrates to be developed: light cement, coating, and glue for ceramics... The said products were tested and characterized by X-ray fluorescence, specific surface, resistance to bending and crushing, etc. We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: déchets de démolition et des chantiers de construction, logiciels de combinaison SIG, valorisation de déchets inertes, enduits, ciment leger, casablanca

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Authors: Alba Prats-Bisbe, Jaume López-Carballo, David Leno-Colorado, Alberto García Molina, Alicia Romero Marquez, Elena Hernández Pena, Eloy Opisso Salleras, Raimon Jané Campos

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Neurological disorders are a leading cause of disability and premature mortality worldwide. Neurorehabilitation (NRHB) is a clinical process aimed at reducing functional impairment, promoting societal participation, and improving the quality of life for affected individuals. Virtual reality (VR) technology is emerging as a promising NRHB support tool. Its immersive nature fosters a strong sense of agency and embodiment, motivating patients to engage in meaningful tasks and increasing adherence to therapy. However, the clinical benefits of VR interventions are challenging to determine due to the high heterogeneity among health applications. This study explores a stepwise development approach for creating VR-based tools to assist individuals with neurological disorders in medical practice, aiming to enhance reproducibility, facilitate comparison, and promote the generalization of findings. Building on previous research, the step-by-step methodology encompasses: Needs Identification– conducting cross-disciplinary meetings to brainstorm problems, solutions, and address barriers. Intervention Definition– target population, set goals, and conceptualize the VR system (equipment and environments). Material Selection and Placement– choose appropriate hardware and software, place the device within the hospital setting, and test equipment. Co-design– collaboratively create VR environments, user interfaces, and data management strategies. Prototyping– develop VR prototypes, conduct user testing, and make iterative redesigns. Usability and Feasibility Assessment– design protocols and conduct trials with stakeholders in the hospital setting. Efficacy Assessment– conduct clinical trials to evaluate outcomes and long-term effects. Cost-Effectiveness Validation– assess reproducibility, sustainability, and balance between costs and benefits. NRHB is complex due to the multifaceted needs of patients and the interdisciplinary healthcare architecture. VR has the potential to support various applications, such as motor skill training, cognitive tasks, pain management, unilateral spatial neglect (diagnosis and treatment), mirror therapy, and ecologically valid activities of daily living. Following this methodology was crucial for launching a VR-based system in a real hospital environment. Collaboration with neuropsychologists lead to develop A) a VR-based tool for cognitive rehabilitation in patients with acquired brain injury (ABI). The system comprises a head-mounted display (HTC Vive Pro Eye) and 7 tasks targeting attention, memory, and executive functions. A desktop application facilitates session configuration, while database records in-game variables. The VR tool's usability and feasibility were demonstrated in proof-of-concept trials with 20 patients, and effectiveness is being tested through a clinical protocol with 12 patients completing 24-session treatment. Another case involved collaboration with nurses and paediatric physiatrists to create B) a VR-based distraction tool during invasive techniques. The goal is to alleviate pain and anxiety associated with botulinum toxin (BTX) injections, blood tests, or intravenous placements. An all-in-one headset (HTC Vive Focus 3) deploys 360º videos to improve the experience for paediatric patients and their families. This study presents a framework for developing clinically relevant and technologically feasible VR-based support tools for hospital settings. Despite differences in patient type, intervention purpose, and VR system, the methodology demonstrates usability, viability, reproducibility and preliminary clinical benefits. It highlights the importance approach centred on clinician and patient needs for any aspect of NRHB within a real hospital setting.

Keywords: neurological disorders, neurorehabilitation, stepwise development approach, virtual reality

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: John G. Vongas, Raghid Al Hajj

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In recent years, there has been a surge in research on empathy across disciplines ranging from management and psychology to philosophy and neuroscience. In organizational behavior, in particular, scholars have become interested in leader empathy given the rise of workplace diversity and the growing perception of leaders as managers of group emotions. It would appear that the current zeitgeist in behavioral and philosophical science is that empathy is a cornerstone of morality and that our world would be better off if only more people – and by extension, more leaders – were empathic. In spite of these claims, however, researchers have used different terminologies to explore empathy, confusing it at times with other related constructs such as emotional intelligence and compassion. Second, extant research that specifies what empathic leaders do and how their behavior affects organizational stakeholders, including themselves, does not devolve from a unifying theoretical framework. These problems plague knowledge development in this important research domain. Therefore, to the authors' best knowledge, this paper provides the first comprehensive review and synthesis of the literature on leader empathy by drawing on disparate yet complementary fields of inquiry. It clarifies empathy from other constructs and presents a theoretical model that elucidates the mechanisms by which a leader’s empathy translates into behaviors that could be either beneficial or harmful to the leaders themselves, as well as to their followers and groups. And third, it specifies the boundary conditions under which a leader’s empathy will become manifest. Finally, it suggests ways in which training could be implemented to improve empathy in practice while also remaining skeptical of its conceptualization as a moral or even effective guide in human affairs.

Keywords: compassion, empathy, leadership, group outcomes

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level, as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, speech emotion recognition, longitudinal biomarker, machine learning

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Authors: M. Trousselard, D. Steiler, C. Drogou, P. van-Beers, G. Lamour, S. N. Crosnier, O. Bouilland, P. Dubost, M. Chennaoui, D. Léger

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According to published data, undersea navigation for long periods (nuclear-powered ballistic missile submarine, SSBN) constitutes an extreme environment in which crews are subjected to multiple stresses, including the absence of natural light, illuminance below 1,000 lux, and watch schedules that do not respect natural chronobiological rhythms, for a period of 60-80 days. These stresses seem clearly detrimental to the submariners’ sleep, with consequences for their affective (seasonal affective disorder-like) and cognitive functioning. In the long term, there are abundant publications regarding the consequences of sleep disruption for the occurrence of organic cardiovascular, metabolic, immunological or malignant diseases. It seems essential to propose countermeasures for the duration of the patrol in order to reduce the negative physiological effects on the sleep and mood of submariners. Light therapy, the preferred treatment for dysfunctions of the internal biological clock and the resulting seasonal depression, cannot be used without data to assist knowledge of submariners’ chronobiology (melatonin secretion curve) during patrols, given the unusual characteristics of their working environment. These data are not available in the literature. The aim of this project was to assess, in the course of two studies, the benefits of two environmental techniques for managing chronobiological stress: techniques for optimizing potential (TOP; study 1)3, an existing programme to help in the psychophysiological regulation of stress and sleep in the armed forces, and dawn and dusk simulators (DDS, study 2). For each experiment, psychological, physiological (sleep) or biological (melatonin secretion) data were collected on D20 and D50 of patrol. In the first experiment, we studied sleep and depressive distress in 19 submariners in an operational setting on board an SSBM during a first patrol, and assessed the impact of TOP on the quality of sleep and depressive distress in these same submariners over the course of a second patrol. The submariners were trained in TOP between the two patrols for a 2-month period, at a rate of 1 h of training per week, and assigned daily informal exercises. Results show moderate disruptions in sleep pattern and duration associated with the intensity of depressive distress. The use of TOP during the following patrol improved sleep and depressive mood only in submariners who regularly practiced the techniques. In light of these limited benefits, we assessed, in a second experiment, the benefits of DDS on chronobiology (daily secretion of melatonin) and depressive distress. Ninety submariners were randomly allocated to two groups, group 1 using DDS daily, and group 2 constituting the control group. Although the placebo effect was not controlled, results showed a beneficial effect on chronobiology and depressive mood for submariners with a morning chronotype. Conclusions: These findings demonstrate the difficulty of practicing the tools of psychophysiological management in real life. They raise the question of the subjects’ autonomy with respect to using aids that involve regular practice. It seems important to study autonomy in future studies, as a cognitive resource resulting from the interaction between internal positive resources and “coping” resources, to gain a better understanding of compliance problems.

Keywords: chronobiology, light therapy, seasonal affective disorder, sleep, stress, stress management, submarine

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: G. S. Thyagaraju, U. P. Kulkarni

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Context-aware computing refers to a general class of computing systems that can sense their physical environment, and adapt their behaviour accordingly. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and personalizes applications. Context-aware services have been introduced into mobile devices, such as PDA and mobile phones. In this paper we are presenting a novel approaches used to realize the context aware mobile. The context aware mobile phone (CAMP) proposed in this paper senses the users situation automatically and provides user context required services. The proposed system is developed by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rough sets theory based decision table. Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the decision table based rules for service recommendation. To exemplify and demonstrate the effectiveness of the proposed methods, the context aware mobile phone is tested for college campus scenario including different locations like library, class room, meeting room, administrative building and college canteen.

Keywords: context aware mobile, fuzzy logic, decision table, Bayesian probability

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Authors: Rashmi D. G., Aishwarya G., Niharika M. K.

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Purpose: Free recall tasks target cognitive and linguistic processes like episodic memory, lexical access and retrieval. Consequently, the free recall paradigm is suitable for assessing memory deterioration caused by aging; this also depends on linguistic factors, including the use of first and second languages and their relative ability. Hence, the present study aimed to determine if aging has an effect on visual and auditory recall abilities. Method: Twenty young adults (mean age: 25.4±0.99) and older adults (mean age: 63.3±3.51) participated in the study. Participants performed a free recall task under two conditions – related and unrelated and two modalities - visual and auditory where they were instructed to recall as many items as possible with no specific order and time limit. Results: Free recall performance was calculated as the mean number of correctly recalled items. Although younger participants recalled a higher number of items, the performance across conditions and modality was variable. Conclusion: In summary, the findings of the present study revealed an age-related decline in the efficiency of episodic memory, which is crucial to remember recent events.

Keywords: recall, episodic memory, aging, modality

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Authors: Lifar M. S., Tereshchenko A. A., Bulgakov A. N., Guda S. A., Guda A. A., Soldatov A. V.

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Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions.

Keywords: artificial intelligence, catalyst, co oxidation, reinforcement learning, dynamic control

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Authors: Antonio Espingardeiro

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It is likely that robots will cross the boundaries of industry into households over the next decades. With demographic challenges worldwide, the future ageing populations will require the introduction of assistive technologies capable of providing, care, human dignity and quality of life through the aging process. Robotics technology has a high potential for being used in the areas of social and healthcare by promoting a wide range of activities such as entertainment, companionship, supervision or cognitive and physical assistance. However, such close Human Robotics Interactions (HRIs) encompass a rich set of ethical scenarios that need to be addressed before Socially Assistive Robots (SARs) reach the global markets. Such interactions with robots may seem a worthy goal for many technical/financial reasons but inevitably require close attention to the ethical dimensions of such interactions. This article investigates the current HRI benchmark of social success. It revises it according to the ethical principles of beneficence, non-maleficence and justice aligned with social care ethos. An extension of such benchmark is proposed based on an empirical study of HRIs with elderly groups.

Keywords: HRI, SARs, social success, benchmark, elderly care

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Authors: Lalitchandra J. Shah, Katarzyna Bialek, Melinda L. Clarke, Jessica L. Jansson

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Handwriting is essential to academic success; however, the current literature is limited in the identification of pre-handwriting skills. The purpose of this study was to identify the pre-handwriting skills, which occupational therapy practitioners deem important to handwriting success, as well as those which aid in intervention planning. The online survey instrument consisted of 33 questions that assessed various skills related to the development of handwriting, as well as captured demographic information. Both occupational therapists and occupational therapy assistants were included in the survey study. The survey found that the respondents were in agreement that purposeful scribbling, the ability of a child to copy (vertical/horizontal lines, circle, squares, and triangles), imitating an oblique cross, cognitive skills (attention, praxis, self-regulation, sequencing), grasp patterns, hand dominance, in hand manipulation skills (shift, translation, rotation), bilateral integration, stabilization of paper, crossing midline, and visual perception were important indicators of handwriting readiness. The results of the survey support existing research regarding the skills necessary for the successful development of handwriting in children.

Keywords: development, handwriting, occupational therapy, visual perceptual skills

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Authors: Y. R. Lai, J. C. Jehng, T. T. Chang

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Team is a popular job design in the management settings. Because people on a team need to work together to complete a lot of tasks, the interaction between team members strongly influences team effectiveness. The study examines the effect of social capital and empowering leadership on team cohesion. There are three facets of social capital: structural facet, relational facet, and cognitive facet. Empowering leadership includes enhancing the meaningfulness of work, fostering participation in decision making, expressing confidence in high performance, and providing autonomy from bureaucratic constraints. Data were collected from 181 team members of 47 teams in the real estate agency industry. The results show that the relational social capital, enhancing the meaningfulness of work, and providing autonomy from bureaucratic constraints are positively related to two dimensions of team cohesion: sense of belonging and feelings of moral. Additionally, expressing confidence in high performance is negatively related to sense of belonging.

Keywords: social capital, empowering leadership, team cohesion, team effectiveness

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Authors: Yu-Lin Chen, Kimberly Koester, Marissa Raymond-Flesh, Anika Thapar, Jay Thapar

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Purpose: Effectively communicating adolescent health topics to teens and their parents is crucial. This study emphasizes critically evaluating the optimal use of artificial intelligence tools (AI), which are increasingly prevalent in disseminating health information. By fostering a deeper understanding of AI voice preference in the context of health, the research aspires to have a ripple effect, enhancing the collective health literacy and decision-making capabilities of both teenagers and their parents. This study explores AI voices' potential within health learning modules for annual well-child visits. We aim to identify preferred voice characteristics and understand factors influencing perceived trustworthiness, ultimately aiming to improve health literacy and decision-making in both demographics. Methods: A cross-sectional study assessed preferences and trust perceptions of AI voices in learning modules among teens (11-18) and their parents/guardians in Northern California. The study involved the development of four distinct learning modules covering various adolescent health-related topics, including general communication, sexual and reproductive health communication, parental monitoring, and well-child check-ups. Participants were asked to evaluate eight AI voices across the modules, considering a set of six factors such as intelligibility, naturalness, prosody, social impression, trustworthiness, and overall appeal, using Likert scales ranging from 1 to 10 (the higher, the better). They were also asked to select their preferred choice of voice for each module. Descriptive statistics summarized participant demographics. Chi-square/t-tests explored differences in voice preferences between groups. Regression models identified factors impacting the perceived trustworthiness of the top-selected voice per module. Results: Data from 104 participants (teen=63; adult guardian = 41) were included in the analysis. The mean age is 14.9 for teens (54% male) and 41.9 for the parent/guardian (12% male). At the same time, similar voice quality ratings were observed across groups, and preferences varied by topic. For instance, in general communication, teens leaned towards young female voices, while parents preferred mature female tones. Interestingly, this trend reversed for parental monitoring, with teens favoring mature male voices and parents opting for mature female ones. Both groups, however, converged on mature female voices for sexual and reproductive health topics. Beyond preferences, the study delved into factors influencing perceived trustworthiness. Interestingly, social impression and sound appeal emerged as the most significant contributors across all modules, jointly explaining 71-75% of the variance in trustworthiness ratings. Conclusion: The study emphasizes the importance of catering AI voices to specific audiences and topics. Social impression and sound appeal emerged as critical factors influencing perceived trustworthiness across all modules. These findings highlight the need to tailor AI voices by age and the specific health information being delivered. Ensuring AI voices resonate with both teens and their parents can foster their engagement and trust, ultimately leading to improved health literacy and decision-making for both groups. Limitations and future research: This study lays the groundwork for understanding AI voice preferences for teenagers and their parents in healthcare settings. However, limitations exist. The sample represents a specific geographic location, and cultural variations might influence preferences. Additionally, the modules focused on topics related to well-child visits, and preferences might differ for more sensitive health topics. Future research should explore these limitations and investigate the long-term impact of AI voice on user engagement, health outcomes, and health behaviors.

Keywords: artificial intelligence, trustworthiness, voice, adolescent

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Yaa Asuaba Duopah, Lisa Moran, Khalifa Elmusharaf, Dervla Kelly

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Background: Research has identified a strong link between stress and drug use behaviours. Also, it has been established that the prolonged use of crack cocaine stimulates emotional, cognitive, neurological, and social changes. This paper examines the psychosocial stressors associated with crack cocaine use. Methodology: The study is qualitative and adopts a critical realist approach. Data was collected through 26 face-to-face, in-depth, semi-structured interviews with people who use crack cocaine. Study participants were recruited through two addiction services using purposive. Participants consisted of 15 males and 11 females between the ages of 24-57 years. Data were analysed using thematic analysis. Results: Cravings, financial hardship, family breakdown, and emotional stimulation were revealed as psychosocial stressors associated with crack cocaine use. Conclusion: Addressing the psychosocial stressors identified in this paper through targeted interventions and supportive policies is crucial for improving the well-being of persons who use crack cocaine. Collaboration between addiction, mental health, and support services is recommended to develop and deliver these interventions.

Keywords: psychological stress, substance misuse disorder, mental health, coping

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Authors: Tamal Roy, Anuradha Bhat

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Cognitive abilities aid fishes in foraging, avoiding predators & locating mates. Factors like predation pressure & habitat complexity govern learning & memory in fishes. This study aims to compare spatial learning & memory across four natural populations of zebrafish. Zebrafish, a small cyprinid inhabits a diverse range of freshwater habitats & this makes it amenable to studies investigating role of native environment in spatial cognitive abilities. Four populations were collected across India from waterbodies with contrasting ecological conditions. Habitat complexity of the water-bodies was evaluated as a combination of channel substrate diversity and diversity of vegetation. Experiments were conducted on populations under controlled laboratory conditions. A square shaped spatial testing arena (maze) was constructed for testing the performance of adult zebrafish. The square tank consisted of an inner square shaped layer with the edges connected to the diagonal ends of the tank-walls by connections thereby forming four separate chambers. Each of the four chambers had a main door in the centre. Each chamber had three sections separated by two windows. A removable coloured window-pane (red, yellow, green or blue) identified each main door. A food reward associated with an artificial plant was always placed inside the left-hand section of the red-door chamber. The position of food-reward and plant within the red-door chamber was fixed. A test fish would have to explore the maze by taking turns and locate the food inside the right-side section of the red-door chamber. Fishes were sorted from each population stock and kept individually in separate containers for identification. At a time, a test fish was released into the arena and allowed 20 minutes to explore in order to find the food-reward. In this way, individual fishes were trained through the maze to locate the food reward for eight consecutive days. The position of red door, with the plant and the reward, was shuffled every day. Following training, an intermission of four days was given during which the fishes were not subjected to trials. Post-intermission, the fishes were re-tested on the 13th day following the same protocol for their ability to remember the learnt task. Exploratory tendencies and latency of individuals to explore on 1st day of training, performance time across trials, and number of mistakes made each day were recorded. Additionally, mechanism used by individuals to solve the maze each day was analyzed across populations. Fishes could be expected to use algorithm (sequence of turns) or associative cues in locating the food reward. Individuals of populations did not differ significantly in latencies and tendencies to explore. No relationship was found between exploration and learning across populations. High habitat-complexity populations had higher rates of learning & stronger memory while low habitat-complexity populations had lower rates of learning and much reduced abilities to remember. High habitat-complexity populations used associative cues more than algorithm for learning and remembering while low habitat-complexity populations used both equally. The study, therefore, helped understand the role of natural ecology in explaining variations in spatial learning abilities across populations.

Keywords: algorithm, associative cue, habitat complexity, population, spatial learning

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Hanadi Mubarak Al-Mubaraki, Michael Busler

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Innovation is the powerful tools for economic growth and diversification, which lead to smart growth. The objective of this paper is to identify the opportunities and challenges of innovation programs discuss and analyse the implementation of the innovation program in the United States (US) and United Kingdom (UK). To achieve the objectives, the research used a mixed methods approach, quantitative (survey), and qualitative (multi-case study) to examine innovation best practices in developed countries. In addition, the selection of 4 interview case studies of innovation organisations based on the best practices and successful implementation worldwide. The research findings indicated the two challenges such as 1) innovation required business ecosystem support to deliver innovation outcomes such as new product and new services, and 2) foster the climate of innovation &entrepreneurship for economic growth and diversification. Although the two opportunities such as 1) sustainability of the innovation events which lead smart growth, and 2) establish the for fostering the artificial intelligence hub entrepreneurship networking at multi-levels. The research adds value to academicians and practitioners such as government, funded organizations, institutions, and policymakers. The authors aim to conduct future research a comparative study of innovation case studies between developed and developing countries for policy implications worldwide. The Originality of This study contributes to current literature about the innovation best practice in developed and developing countries.

Keywords: economic development, technology transfer, entrepreneurship, innovation program

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Authors: Ruwan Gunawardane

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Second language teaching and learning is a complex process in which various factors are identified as having a negative impact on the competency in English among undergraduates of Sri Lanka. One such issue is the lack of intrinsic motivation among them to learn English despite the fact that they all know the importance of English. This study attempted to ascertain how the intrinsic motivation of undergraduates to learn English can be improved through reading out of curiosity. Humans are curious by nature, and cognitive psychology says that curiosity facilitates learning, memory, and motivation. The researcher carried out this study during the closure of universities due to the outbreak of the coronavirus through ‘Online Reading Café’, an online reading programme introduced by himself. He invited 1166 students of the Faculty of Science, University of Ruhuna, to read 50 articles taken from CNN and the BBC and posted at least two to three articles on the LMS of the faculty almost every day over a period of 23 days. The themes of the articles were based on the universe, exploration of planets, scientific experiments, evolution, etc., and the students were encouraged to collect as many words, phrases, and sentence structures as possible while reading and to form meaningful sentences using them. The data obtained through the students’ feedback was qualitatively analyzed. It was found that these undergraduates were interested in reading something out of curiosity, due to which intrinsic motivation is enhanced, and it facilitates competence in L2.

Keywords: English, competence, reading, curiosity

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: T. Arshad

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A correlation study was done to explore the relationship of intrusiveness, appraisal and thought control strategies in patients with Obsessive Compulsive Disorder. Theoretical frame work for the present study was Salkovskis (1985) cognitive model of obsessive compulsive disorder. Sample of 100 patients (men=48, women=52) of age 14-62 years (M=32.13, SD=10.37) was recruited from hospitals of Lahore, Pakistan. Revised Obsessional Intrusion Inventory, Stress Appraisal Measure, Thought Control Questionnaire and Symptoms Checklist-R were self-administered. Findings revealed that intrusiveness is correlated with appraisals (controllable by self, controllable by others, uncontrollable, stressfulness) and thought control strategy (punishment). Furthermore, appraisals (uncontrollable, stressfulness, controllable by others) were emerged as strong predictors for different through control strategies (distraction, punishment and social control). Moreover, men have higher frequency of intrusion, whereas women were frequently using social control as thought control strategy. Results implied that intrusiveness, appraisals (controllable by others, uncontrollable, stressfulness) and thought control strategy (punishment) are related which maintains the disorder.

Keywords: appraisal, intrusiveness, obsessive compulsive disorder, thought control strategies

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Authors: Valerie Yocco Rossi

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Objective: The author will share why differentiation is necessary for all classrooms as well as strategies for differentiating content, process, and product. Through learning how to differentiate, teachers will be able to create activities and assessments to meet the abilities, readiness levels, and interests of all learners. Content and Purpose: This work will focus on how to create a learning experience for students that recognizes their different interests, abilities, and readiness levels by differentiating content, process, and product. Likewise, the best learning environments allow for choice. Choice boards allow students to select tasks based on interests. There can be challenging and basic tasks to meet the needs of various abilities. Equally, rubrics allow for personalized and differentiated assessments based on readiness levels and cognitive abilities. The principals of DI help to create a classroom where all students are learning to the best of their abilities. Outcomes: After reviewing the work, readers will be able to (1) identify the benefits of differentiated instruction; (2) convert traditional learning activities to differentiated ones; (3) differentiate, writing-based assessments.

Keywords: differentiation, personalized learning, design, instructional strategies

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Authors: Bisola Stella Sonde

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Digital innovation has emerged as a pivotal driver of business transformation in the contemporary landscape. This case study research explores the dynamic interplay between digital innovation and the profound metamorphosis of businesses across industries. It delves into the multifaceted dimensions of digital innovation, elucidating its impact on organizational structures, customer experiences, and operational paradigms. The study investigates real-world instances of businesses harnessing digital technologies to enhance their competitiveness, agility, and sustainability. It scrutinizes the strategic adoption of digital platforms, data analytics, artificial intelligence, and emerging technologies as catalysts for transformative change. The cases encompass a diverse spectrum of industries, spanning from traditional enterprises to disruptive startups, offering insights into the universal relevance of digital innovation. Moreover, the research scrutinizes the challenges and opportunities posed by the digital era, shedding light on the intricacies of managing cultural shifts, data privacy, and cybersecurity concerns in the pursuit of innovation. It unveils the strategies that organizations employ to adapt, thrive, and lead in the era of digital disruption. In summary, this case study research underscores the imperative of embracing digital innovation as a cornerstone of business transformation. It offers a comprehensive exploration of the contemporary digital landscape, offering valuable lessons for organizations striving to navigate the ever-evolving terrain of the digital age.

Keywords: business transformation, digital innovation, emerging technologies, organizational structures

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Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

Procedia PDF Downloads 165