Search results for: motor intelligence

Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Shereen Ismail Fawaz, Shin-Ichi Izumi, Nouran Mohamed Salah, Heba G. Saber, Ibrahim Mohamed Roushdi

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Background: Multiple sclerosis (MS) is a chronic, inflammatory, mainly demyelinating disease of the central nervous system, more common in young adults. Cerebellar involvement is one of the most disabling lesions in MS and is usually a sign of disease progression. It plays a major role in the planning, initiation, and organization of movement via its influence on the motor cortex and corticospinal outputs. Therefore, it contributes to controlling movement, motor adaptation, and motor learning, in addition to its vast connections with other major pathways controlling balance, such as the cerebellopropriospinal pathways and cerebellovestibular pathways. Hence, trying to stimulate the cerebellum by facilitatory protocols will add to our motor control and balance function. Non-invasive brain stimulation, both repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), has recently emerged as effective neuromodulators to influence motor and nonmotor functions of the brain. Anodal tDCS has been shown to improve motor skill learning and motor performance beyond the training period. Similarly, rTMS, when used at high frequency (>5 Hz), has a facilitatory effect on the motor cortex. Objective: Our aim was to determine the effect of high-frequency rTMS over the cerebellum in improving balance and functional ambulation of multiple sclerosis patients with Ataxia. Patients and methods: This was a randomized single-blinded placebo-controlled prospective trial on 40 patients. The active group (N=20) received real rTMS sessions, and the control group (N=20) received Sham rTMS using a placebo program designed for this treatment. Both groups received 12 sessions of high-frequency rTMS over the cerebellum, followed by an intensive exercise training program. Sessions were given three times per week for four weeks. The active group protocol had a frequency of 10 Hz rTMS over the cerebellar vermis, work period 5S, number of trains 25, and intertrain interval 25s. The total number of pulses was 1250 pulses per session. The control group received Sham rTMS using a placebo program designed for this treatment. Both groups of patients received an intensive exercise program, which included generalized strengthening exercises, endurance and aerobic training, trunk abdominal exercises, generalized balance training exercises, and task-oriented training such as Boxing. As a primary outcome measure the Modified ICARS was used. Static Posturography was done with: Patients were tested both with open and closed eyes. Secondary outcome measures included the expanded Disability Status Scale (EDSS) and 8 Meter walk test (8MWT). Results: The active group showed significant improvements in all the functional scales, modified ICARS, EDSS, and 8-meter walk test, in addition to significant differences in static Posturography with open eyes, while the control group did not show such differences. Conclusion: Cerebellar high-frequency rTMS could be effective in the functional improvement of balance in MS patients with ataxia.

Keywords: brain neuromodulation, high frequency rTMS, cerebellar stimulation, multiple sclerosis, balance rehabilitation

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Authors: Chien-Ho Lin, Ho-Ching Yang, Barbara Knowlton, Shin-Leh Huang, Ming-Chang Chiang

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Practicing motor tasks arranged in an interleaved order (interleaved practice, or IP) generally leads to better learning than practicing tasks in a repetitive order (repetitive practice, or RP), an example of how desirable difficulty during practice benefits learning. Greater difficulty during practice, e.g. IP, is associated with greater brain activity measured by higher blood-oxygen-level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) in the sensorimotor areas of the brain. In this study resting-state fMRI was applied to investigate whether increase in resting-state brain connectivity immediately after practice predicts the benefit of desirable difficulty to motor learning. 26 healthy adults (11M/15F, age = 23.3±1.3 years) practiced two sets of three sequences arranged in a repetitive or an interleaved order over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day, fMRI data were acquired in a resting state after practice. The resting-state fMRI data was decomposed using a group-level spatial independent component analysis (ICA), yielding 9 independent components (IC) matched to the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipito-temporal network, and the frontal network. A weighted resting-state functional connectivity (wRSFC) was then defined to incorporate information from within- and between-network brain connectivity. The within-network functional connectivity between a voxel and an IC was gauged by a z-score derived from the Fisher transformation of the IC map. The between-network connectivity was derived from the cross-correlation of time courses across all possible pairs of ICs, leading to a symmetric nc x nc matrix of cross-correlation coefficients, denoted by C = (pᵢⱼ). Here pᵢⱼ is the extremum of cross-correlation between ICs i and j; nc = 9 is the number of ICs. This component-wise cross-correlation matrix C was then projected to the voxel space, with the weights for each voxel set to the z-score that represents the above within-network functional connectivity. The wRSFC map incorporates the global characteristics of brain networks measured by the between-network connectivity, and the spatial information contained in the IC maps measured by the within-network connectivity. Pearson correlation analysis revealed that greater IP-minus-RP difference in wRSFC was positively correlated with the RP-minus-IP difference in the response time on Day 5, particularly in brain regions crucial for motor learning, such as the right dorsolateral prefrontal cortex (DLPFC), and the right premotor and supplementary motor cortices. This indicates that enhanced resting brain connectivity during the early phase of memory consolidation is associated with enhanced learning following interleaved practice, and as such wRSFC could be applied as a biomarker that measures the beneficial effects of desirable difficulty on motor sequence learning.

Keywords: desirable difficulty, functional magnetic resonance imaging, independent component analysis, resting-state networks

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Authors: Bashiru Olayinka Adisa, Najeem Lateef

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The integrated knowledge in the application of mechanical engineering, microprocessor and electronic sensor technologies is becoming the basic skill of a modern engineer in machinery based processes. To meet this objective, we have developed a cross-disciplinary industrial training to teach essential hard technical and soft project skills to the mechanical engineering students in mid-curriculum. Ten groups of students were selected to participate in a 150 hour program. The students were required to design and build a robot with ability to follow tracks and pick/place target blocks in specific locations. The students were trained to integrate the knowledge of computer aid design, electronics, sensor theories and motor technology to fabricate a workable robot as a major outcome of this course. On completion of the project, students competed for top robot honors by demonstrating their robots' movements and performance in pick/place to a panel of judges.

Keywords: electronics, sensor theories and motor, robot, technology

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Authors: Huai-Cong Liu，Tae Chul Jeong，Ju Lee

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This paper describes characteristic analysis of a synchronous reluctance motor (SynRM)’s rotor with the Multi-segment and Multi-layer structure. The magnetic-saturation phenomenon in SynRM is often appeared. Therefore, when modeling analysis of SynRM the calculation of nonlinear magnetic field needs to be considered. An important influence factor on the convergence process is how to determine the relative permeability. An improved method, which ensures the calculation, is convergence by linear iterative method for saturated magnetic field. If there are inflection points on the magnetic curve,an optimum convergence method of solution for nonlinear magnetic field was provided. Then the equivalent magnetic circuit is calculated, and d,q-axis inductance can be got. At last, this process is applied to design a 7.5Kw SynRM and its validity is verified by comparing with the result of finite element method (FEM) and experimental test data.

Keywords: SynRM, magnetic-saturation, magnetic circuit, analytical modeling

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Authors: Amina Akhtar, Rory O'Connor

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Introduction: There remains significant morbidity and mortality in young pedestrians hit by motor vehicles, even in the era of pedestrian crossings and speed limits. The aim of this study was to compare incidence and injury severity of motor vehicle-related pedestrian trauma according to time of day and season in a young population, based on the supposition that injuries would be more prevalent during dusk and dawn and during autumn and winter. Methods: Data was retrieved for patients between 10-25 years old from the National Trauma Audit and Research Network (TARN) database who had been involved as pedestrians in motor vehicle accidents between 2015-2020. The incidence of injuries, their severity (using the Injury Severity Score [ISS]), hospital transfer time, and mortality were analysed according to the hours of daylight, darkness, and season. Results: The study identified a seasonal pattern, showing that autumn was the predominant season and led to 34.9% of injuries, with a further 25.4% in winter in comparison to spring and summer, with 21.4% and 18.3% of injuries, respectively. However, visibility alone was not a sufficient factor as 49.5% of injuries occurred during the time of darkness, while 50.5% occurred during daylight. Importantly, the greatest injury rate (number of injuries/hour) occurred between 1500-1630, correlating to school pick-up times. A further significant relationship between injury severity score (ISS) and daylight was demonstrated (p-value= 0.0124), with moderate injuries (ISS 9-14) occurring most commonly during the day (72.7%) and more severe injuries (ISS>15) occurred during the night (55.8%). Conclusion: We have identified a relationship between time of day and the frequency and severity of pedestrian trauma in young people. In addition, particular time groupings correspond to the greatest injury rate, suggesting that reduced visibility coupled with school pick-up times may play a significant role. This could be addressed through a targeted public health approach to implementing change. We recommend targeted public health measures to improve road safety that focus on these times and that increase the visibility of children combined with education for drivers.

Keywords: major trauma, paediatric trauma, road traffic accidents, diurnal pattern

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Authors: Marija Zulić, Vanda Hájková, Nina Brkić–Jovanović, Srećko Potić, Sanja Tomić

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The name cerebral palsy comes from the word cerebrum, which means the brain and the word palsy, which means seizure, and essentially refers to the movement disorder. In the clinical picture of cerebral palsy, basic neuromotor disorders are associated with other various disorders: behavioural, intellectual, speech, sensory, epileptic seizures, and bone and joint deformities. Motor speech disorders are among the most common difficulties present in people with cerebral palsy. Social participation represents an interaction between an individual and their social environment. Quality of social participation of the students with cerebral palsy at school is an important indicator of their successful participation in adulthood. One of the most important skills for the undisturbed social participation is ability of good communication. The aim of the study was to determine relation between social participation of students with cerebral palsy and presence of their speech impairment in primary schools in the Czech Republic. The study was performed in the Czech Republic in mainstream schools and schools established for the pupils with special education needs. We analysed 75 children with cerebral palsy aged between six and twelve years attending up to sixth grade by using the first and the third part of the school function assessment questionnaire as the main instrument. The other instrument we used in the research is the Gross motor function classification system–five–level classification system, which measures degree of motor functions of children and youth with cerebral palsy. Funding for this study was provided by the Grant Agency of Charles University in Prague.

Keywords: cerebral palsy, social participation, speech disorders, The Czech Republic, the school function assessment

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Authors: Ali R. Alshawawreh, Francisco Liébana-Cabanillas, Francisco J. Blanco-Encomienda

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Collaboration between marketing and business intelligence (BI) is crucial in today's ever-evolving business landscape. These two domains play pivotal roles in molding customers' experiential knowledge. Marketing insights offer valuable information regarding customer needs, preferences, and behaviors. Conversely, BI facilitates data-driven decision-making, leading to heightened operational efficiency, product quality, and customer satisfaction. Customer experiential knowledge (CEK) encompasses customers' implicit comprehension of consumption experiences influenced by diverse factors, including social and cultural influences. This study primarily focuses on telecommunications companies in Jordan, scrutinizing how experiential customer knowledge mediates the relationship between marketing intelligence and business intelligence. Drawing on theoretical frameworks such as the resource-based view (RBV) and service-dominant logic (SDL), the research aims to comprehend how organizations utilize their resources, particularly knowledge, to foster Evolution. Employing a quantitative research approach, the study collected and analyzed primary data to explore hypotheses. Structural equation modeling (SEM) facilitated by Smart PLS software evaluated the relationships between the constructs, followed by mediation analysis to assess the indirect associations in the model. The study findings offer insights into the intricate dynamics of organizational Creation, uncovering the interconnected relationships between business intelligence, customer experiential knowledge-based innovation (CEK-DI), marketing intelligence (MI), and product and service innovation (PSI), underscoring the pivotal role of advanced intelligence capabilities in developing innovative practices rooted in a profound understanding of customer experiences. Furthermore, the positive impact of BI on PSI reaffirms the significance of data-driven decision-making in shaping the innovation landscape. The significant impact of CEK-DI on PSI highlights the critical role of customer experiences in driving an organization. Companies that actively integrate customer insights into their opportunity creation processes are more likely to create offerings that match customer expectations, which drives higher levels of product and service sophistication. Additionally, the positive and significant impact of MI on CEK-DI underscores the critical role of market insights in shaping evolutionary strategies. While the relationship between MI and PSI is positive, the slightly weaker significance level indicates a subtle association, suggesting that while MI contributes to the development of ideas, In conclusion, the study emphasizes the fundamental role of intelligence capabilities, especially artificial intelligence, emphasizing the need for organizations to leverage market and customer intelligence to achieve effective and competitive innovation practices. Collaborative efforts between marketing and business intelligence serve as pivotal drivers of development, influencing customer experiential knowledge and shaping organizational strategies and practices. Future research could adopt longitudinal designs and gather data from various sectors to offer broader insights. Additionally, the study focuses on the effects of marketing intelligence, business intelligence, customer experiential knowledge, and innovation, but other unexamined variables may also influence innovation processes. Future studies could investigate additional factors, mediators, or moderators, including the role of emerging technologies like AI and machine learning in driving innovation.

Keywords: marketing intelligence, business intelligence, product, customer experiential knowledge-driven innovation

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

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With the evolution of the productive processes, demonstrated mainly by the presence every time larger of the irrigation and to crescent it disputes for water, accompanied by your shortage (distances every time larger), there is need to project facilities that can provide supply of water with larger speed and efficiency. Being like this, the presence of hydraulic pumps in an irrigation project or water supply for small communities, is of highest importance, and the knowledge of the fundamental parts to your good operation it deserves the due attention and care. Hydraulic pumps are machines of flow, whose function is to supply energy for the water, in order to press down her, through the conversion of mechanical energy of your originating from rotor a motor the combustion or of an electric motor. This way, the hydraulic pumps are had as generating hydraulic machines. The objective of this work was to project and to build a radial centrifugal pump for agricultural application in small communities.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigation

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Authors: Gerhard Gritsch, Ana Skupch, Manfred Hartmann, Wolfgang Frühwirt, Hannes Perko, Dieter Grossegger, Tilmann Kluge

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Rehabilitation of stroke patients is dominated by classical physiotherapy. Nowadays, a field of research is the application of neurofeedback techniques in order to help stroke patients to get rid of their motor impairments. Especially, if a certain limb is completely paralyzed, neurofeedback is often the last option to cure the patient. Certain exercises, like the imagination of the impaired motor function, have to be performed to stimulate the neuroplasticity of the brain, such that in the neighboring parts of the injured cortex the corresponding activity takes place. During the exercises, it is very important to keep the motivation of the patient at a high level. For this reason, the missing natural feedback due to a movement of the effected limb may be replaced by a synthetic feedback based on the motor-related brain function. To generate such a synthetic feedback a system is needed which measures, detects, localizes and visualizes the motor related µ-rhythm. Fast therapeutic success can only be achieved if the feedback features high specificity, comes in real-time and without large delay. We describe such an approach that offers a 3D visualization of µ-rhythms in real time with a delay of 500ms. This is accomplished by combining smart EEG preprocessing in the frequency domain with source localization techniques. The algorithm first selects the EEG channel featuring the most prominent rhythm in the alpha frequency band from a so-called motor channel set (C4, CZ, C3; CP6, CP4, CP2, CP1, CP3, CP5). If the amplitude in the alpha frequency band of this certain electrode exceeds a threshold, a µ-rhythm is detected. To prevent detection of a mixture of posterior alpha activity and µ-activity, the amplitudes in the alpha band outside the motor channel set are not allowed to be in the same range as the main channel. The EEG signal of the main channel is used as template for calculating the spatial distribution of the µ - rhythm over all electrodes. This spatial distribution is the input for a inverse method which provides the 3D distribution of the µ - activity within the brain which is visualized in 3D as color coded activity map. This approach mitigates the influence of lid artifacts on the localization performance. The first results of several healthy subjects show that the system is capable of detecting and localizing the rarely appearing µ-rhythm. In most cases the results match with findings from visual EEG analysis. Frequent eye-lid artifacts have no influence on the system performance. Furthermore, the system will be able to run in real-time. Due to the design of the frequency transformation the processing delay is 500ms. First results are promising and we plan to extend the test data set to further evaluate the performance of the system. The relevance of the system with respect to the therapy of stroke patients has to be shown in studies with real patients after CE certification of the system. This work was performed within the project ‘LiveSolo’ funded by the Austrian Research Promotion Agency (FFG) (project number: 853263).

Keywords: real-time EEG neuroimaging, neurofeedback, stroke, EEG–signal processing, rehabilitation

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Authors: Najah Kalifah Almazmomi

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In the time of artificial intelligence (AI), there is a need to know the determinants of success in business management, which are taking on a new dimension. This research purports to scrutinize the Critical Success Factors (CSFs) that drive and ignite the fire of success to help uncover the subtle and profound dynamics that might be operative in organizations. By means of a systematic literature review and a number of empirical methods, the paper is aimed at determining and assessing the key aspects of CSFs, putting emphasis on their role and meaning in the context of AI technology adoption. Some central features such as leadership ways, innovation models, strategic thinking methodologies, organizational culture transformations, and human resource management approaches are compared and contrasted with the AI-driven revolution. Additionally, this research will explore the interactive effects of these factors and their joint impact on the success, survival, and flexibility of a business in the current environment, which is changing due to AI development. Through the use of different qualitative and quantitative methodologies, the research concludes that the findings are significant in understanding the relative roles of individual CSFs and in studying the interactions between them in such an AI-enabled business environment.

Keywords: critical success factors, business and management, artificial intelligence, leadership strategies

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Authors: Raimo P. Hämäläinen, Juha Törmänen, Esa Saarinen

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Systems thinking has been acknowledged as an important approach in the strategy and management literature ever since the seminal works of Ackhoff in the 1970´s and Senge in the 1990´s. The early literature was very much focused on structures and organizational dynamics. Understanding systems is important but making improvements also needs ways to understand human behavior in systems. Peter Senge´s book The Fifth Discipline gave the inspiration to the development of the concept of Systems Intelligence. The concept integrates the concepts of personal mastery and systems thinking. SI refers to intelligent behavior in the context of complex systems involving interaction and feedback. It is a competence related to the skills needed in strategy and the environment of modern industrial engineering and management where people skills and systems are in an increasingly important role. The eight factors of Systems Intelligence have been identified from extensive surveys and the factors relate to perceiving, attitude, thinking and acting. The personal self-evaluation test developed consists of 32 items which can also be applied in a peer evaluation mode. The concept and test extend to organizations too. One can talk about organizational systems intelligence. This paper reports the results of an extensive survey based on peer evaluation. The results show that systems intelligence correlates positively with professional performance. People in a managerial role score higher in SI than others. Age improves the SI score but there is no gender difference. Top organizations score higher in all SI factors than lower ranked ones. The SI-tests can also be used as leadership and management development tools helping self-reflection and learning. Finding ways of enhancing learning organizational development is important. Today gamification is a new promising approach. The items in the SI test have been used to develop an interactive card game following the Topaasia game approach. It is an easy way of engaging people in a process which both helps participants see and approach problems in their organization. It also helps individuals in identifying challenges in their own behavior and in improving in their SI.

Keywords: gamification, management competence, organizational learning, systems thinking

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Authors: Ayomide Oyedele

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In the dynamic landscape of cybersecurity, "Strategic Cyber Sentinel" emerges as a revolutionary framework, transcending traditional approaches. This paper pioneers a holistic strategy, weaving together threat intelligence, machine learning, and adaptive defenses. Through meticulous real-world simulations, we demonstrate the unprecedented resilience of our framework against evolving cyber threats. "Strategic Cyber Sentinel" redefines proactive threat mitigation, offering a robust defense architecture poised for the challenges of tomorrow.

Keywords: cybersecurity, resilience, threat intelligence, machine learning, adaptive defenses

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Authors: Buchh Aqsa, Inayat Umar

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Background- Clonidine, α 2 agonist, possesses several properties to make it valuable adjuvant for spinal anesthesia. The study was aimed to evaluate the clinical effects of oral clonidine premedication for laparoscopic gynecological procedures under subarachnoid block. Patients and method- Sixtyfour adult female patients of ASA physical status I and II, aged 25 to 45 years and scheduled for laparoscopic gynecological procedures under the subarachnoid block, were randomized into two comparable equal groups of 32 patients each to received either oral clonidine, 100 µg (Group I) or placebo (Group II), 90 minutes before the procedure. Subarachnoid block was established with of 3.5 ml of 0.5% hyperbaric bupivacaine in all patients. Onset and duration of sensory and motor block, maximum cephalad level, and the regression time to reach S1 sensory level were assessed as primary end points. Sedation, hemodynamic variability, and respiratory depression or any other side effects were evaluated as secondary outcomes. Results- The demographic profile was comparable. The intraoperative hemodynamic parameters showed significant differences between groups. Oral clonidine was accelerated the onset time of sensory and motor blockade and extended the duration of sensory block (216.4 ± 23.3 min versus 165 ± 37.2 min, P <0.05). The duration of motor block showed no significant difference. The sedation score was more than 2 in the clonidine group as compared to the control group. Conclusion- Oral clonidine premedication has extended the duration of sensory analgesia with arousable sedation. It also prevented the post spinal shivering of the subarachnoid block.

Keywords: oral clonidine, subarachnoid block, sensory analgesia, laparoscopic gynaecological

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Authors: Duy Nhat Tran, Van Tien Pham, Quang Trung Trinh, Tien Hai Tran, Van Cong Bui

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In recent years, artificial intelligence and the Internet of Things have experienced significant advancements. Collecting image data and real-time analysis and processing of tasks have become increasingly popular in various aspects of life. Optical scanning devices are widely used to observe and analyze different environments, whether fixed outdoors, mounted on mobile devices, or used in unmanned aerial vehicles. As a result, the interaction between the physical environment and these devices has become more critical in terms of safety. Two commonly used methods for addressing these challenges are active and passive approaches. Each method has its advantages and disadvantages, but combining both methods can lead to higher efficiency. One solution is to utilize direct-drive motors for position control and real-time feedback within the operational range to determine appropriate control parameters with high precision. If the maximum motor torque is smaller than the inertial torque and the rotor reaches the operational limit, the spring system absorbs the impact force. Numerous experiments have been conducted to demonstrate the effectiveness of device protection during operation.

Keywords: optical device, collision safety, collision absorption, precise mechanics

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Authors: Mohammed Nasser Al-Suqri

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Former information-seeking models are proposed more than two decades ago. These already existed models were given prior to the evolution of digital information era and Artificial Intelligence (AI) technologies. Lack of current information seeking models within Library and Information Studies resulted in fewer advancements for teaching students about information-seeking behaviors, design of library tools and services. In order to better facilitate the aforementioned concerns, this study aims to propose state-of-the-art model while focusing on the information seeking behavior of library users in the Sultanate of Oman. This study aims for the development, designing and contextualizing the real-time user-centric information seeking model capable of enhancing information needs and information usage along with incorporating critical insights for the digital library practices. Another aim is to establish far-sighted and state-of-the-art frame of reference covering Artificial Intelligence (AI) while synthesizing digital resources and information for optimizing information-seeking behavior. The proposed study is empirically designed based on a mix-method process flow, technical surveys, in-depth interviews, focus groups evaluations and stakeholder investigations. The study data pool is consist of users and specialist LIS staff at 4 public libraries and 26 academic libraries in Oman. The designed research model is expected to facilitate LIS by assisting multi-dimensional insights with AI integration for redefining the information-seeking process, and developing a technology rich model.

Keywords: artificial intelligence, information seeking, information behavior, information seeking models, libraries, Sultanate of Oman

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Authors: Camila Penido Gomes

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Over the past decade, an emerging body of research has been pointing to artificial intelligence´s great potential to improve the use of open data, increase transparency and curb corruption in the public sector. Nonetheless, studies on this subject are scant and usually lack evidence to validate AI-based technologies´ effectiveness in addressing corruption, especially in developing countries. Aiming to fill this void in the literature, this paper sets out to examine how AI has been deployed by civil society to improve the use of open data and prevent congresspeople from misusing public resources in Brazil. Building on the current debates and carrying out a systematic literature review and extensive document analyses, this research reveals that AI should not be deployed as one silver bullet to fight corruption. Instead, this technology is more powerful when adopted by a multidisciplinary team as a civic tool in conjunction with other strategies. This study makes considerable contributions, bringing to the forefront discussion a more accurate understanding of the factors that play a decisive role in the successful implementation of AI-based technologies in anti-corruption efforts.

Keywords: artificial intelligence, civil society organization, corruption, open data, transparency

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Jui-Chen Huang

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This research used artificial intelligence method to explore elderly’s opinions on the reuse of telecare, its effect on their service quality, satisfaction and the relationship between customer perceived value and intention to reuse. This study conducted a questionnaire survey on the elderly. A total of 124 valid copies of a questionnaire were obtained. It adopted Backpropagation Network (BPN) to propose an effective and feasible analysis method, which is different from the traditional method. Two third of the total samples (82 samples) were taken as the training data, and the one third of the samples (42 samples) were taken as the testing data. The training and testing data RMSE (root mean square error) are 0.022 and 0.009 in the BPN, respectively. As shown, the errors are acceptable. On the other hand, the training and testing data RMSE are 0.100 and 0.099 in the regression model, respectively. In addition, the results showed the service quality has the greatest effects on the intention to reuse, followed by the satisfaction, and perceived value. This result of the Backpropagation Network method is better than the regression analysis. This result can be used as a reference for future research.

Keywords: artificial intelligence, backpropagation network (BPN), elderly, reuse, telecare

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Authors: Reema Alghamdi, Rasees Aleisa, Layan Sukkar

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The primary objective of this research is to conduct an examination of the Flo menstrual cycle application. We do that by evaluating the user experience and their satisfaction with integrated AI features. The study seeks to gather data from primary resources, primarily through surveys, to gather different insights about the application, like its usability functionality in addition to the overall user satisfaction. The focus of our project will be particularly directed towards the impact and user perspectives regarding the integration of artificial intelligence features within the application, contributing to an understanding of the holistic user experience.

Keywords: period, women health, machine learning, AI features, menstrual cycle

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Authors: Abdulaziz Fageeh

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This study investigates the integration of artificial intelligence (AI) within the landscape of language learning and teaching, exploring its potential benefits and challenges. Employing a mixed-methods approach, the research draws upon a comprehensive literature review, case studies, user reviews, and in-depth interviews with educators and students. Findings demonstrate that AI tools, including language learning apps and writing assistants, can enhance personalization, improve writing skills, and increase accessibility to language learning resources. However, the study also highlights concerns regarding over-reliance on AI, potential accuracy and reliability issues, and ethical implications such as data privacy and potential bias. User and educator perspectives emphasize the importance of balancing AI with traditional teaching methods, fostering critical thinking skills, and addressing potential misuse. The study concludes by underscoring the need for ongoing research and development to ensure responsible AI integration in language learning, focusing on pedagogical strategies, ethical frameworks, and the long-term impact of AI on learning outcomes.

Keywords: artificial intelligence, language learning, education, technology, ethical considerations, user perceptions

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Authors: Mohammad Talebi

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Supply chain administration could be a concern for all the countries within the world, whereas there's no special approach towards supportability. Generally, for one decade, manufactured insights applications in keen supply chains have found a key part. In this paper, applications of artificial intelligence in supply chain management have been clarified, and towards Canadian plans for smart supply chain management (SCM), a few notes have been suggested. A hierarchical framework for smart SCM might provide a great roadmap for decision-makers to find the most appropriate approach toward smart SCM. Within the system of decision-making, all the levels included in the accomplishment of smart SCM are included. In any case, more considerations are got to be paid to available and needed infrastructures.

Keywords: smart SCM, AI, SSCM, procurement

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Authors: Mahdi Shaghaghi, Amir Ghaebi, Fariba Ahmadi

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Purpose: The main purpose of the present research is to analyze the knowledge creation environment at a Iranian University (Alzahra University) as a typical University in Iran, using a combination of the i-System and Ba models. This study is necessary for understanding the determinants of knowledge creation at Alzahra University as a typical University in Iran. Methodology: To carry out the present research, which is an applied study in terms of purpose, a descriptive survey method was used. In this study, a combination of the i-System and Ba models has been used to analyze the knowledge creation environment at Alzahra University. i-System consists of 5 constructs including intervention (input), intelligence (process), involvement (process), imagination (process), and integration (output). The Ba environment has three pillars, namely the infrastructure, the agent, and the information. The integration of these two models resulted in 11 constructs which were as follows: intervention (input), infrastructure-intelligence, agent-intelligence, information-intelligence (process); infrastructure-involvement, agent-involvement, information-involvement (process); infrastructure-imagination, agent-imagination, information-imagination (process); and integration (output). These 11 constructs were incorporated into a 52-statement questionnaire and the validity and reliability of the questionnaire were examined and confirmed. The statistical population included the faculty members of Alzahra University (344 people). A total of 181 participants were selected through the stratified random sampling technique. The descriptive statistics, binomial test, regression analysis, and structural equation modeling (SEM) methods were also utilized to analyze the data. Findings: The research findings indicated that among the 11 research constructs, the levels of intervention, information-intelligence, infrastructure-involvement, and agent-imagination constructs were average and not acceptable. The levels of infrastructure-intelligence and information-imagination constructs ranged from average to low. The levels of agent-intelligence and information-involvement constructs were also completely average. The level of infrastructure-imagination construct was average to high and thus was considered acceptable. The levels of agent-involvement and integration constructs were above average and were in a highly acceptable condition. Furthermore, the regression analysis results indicated that only two constructs, viz. the information-imagination and agent-involvement constructs, positively and significantly correlate with the integration construct. The results of the structural equation modeling also revealed that the intervention, intelligence, and involvement constructs are related to the integration construct with the complete mediation of imagination. Discussion and conclusion: The present research suggests that knowledge creation at Alzahra University relatively complies with the combination of the i-System and Ba models. Unlike this model, the intervention, intelligence, and involvement constructs are not directly related to the integration construct and this seems to have three implications: 1) the information sources are not frequently used to assess and identify the research biases; 2) problem finding is probably of less concern at the end of studies and at the time of assessment and validation; 3) the involvement of others has a smaller role in the summarization, assessment, and validation of the research.

Keywords: i-System, Ba model , knowledge creation , knowledge management, knowledge creation environment, Iranian Universities

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Authors: Minhee Son, Kyung Nam Kim

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This study explores the plan of the Korean and Japanese governments to transition into the hydrogen economy. Two motor companies, Hyundai Motor Company from Korea and Toyota from Japan, released the Hydrogen Fuel Cell Vehicle to monopolize the green energy automobile market. Although, they are the main countries which emit greenhouse gas, hydrogen energy can bring from a certain industry places, such as chemical plants and steel mills. Recent, the two countries have been focusing on the hydrogen industry including a fuel cell vehicle, a hydrogen station, a fuel cell plant, a residential fuel cell. The purpose of this paper is to find out the differences of the policies in the two countries to be hydrogen societies. We analyze the behavior of the public and private sectors in Korea and Japan about hydrogen energy and fuel cells for the transition of the hydrogen economy. Finally we show the similarities and differences of both countries in hydrogen fuel cells. And some cities have feature such as Hydrogen cities. Hydrogen energy can make impact environmental sustainability.

Keywords: fuel cell, hydrogen city, hydrogen fuel cell vehicle, hydrogen station, hydrogen energy

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Authors: Nuno Da Camara, Victor Dulewicz, Malcolm Higgs

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Factors on employee engagement: In particular, although theorists have described the critical role of emotional cognition of the workplace environment as antecedents to employee engagement, empirical research on the impact of emotional cognition on employee engagement is limited. However, previous researchers have typically provided evidence of the link between emotional cognition of the workplace environment and workplace attitudes such as job satisfaction and organisational commitment. This study therefore aims to investigate the impact of emotional cognition of job, role, leader and organisation domains of the work environment – as represented by measures of psychological climate and organizational emotional intelligence (OEI) - on employee engagement. The research is based on a quantitative cross-sectional survey of employees in a UK charity organization (n=174). The research instruments applied include the psychological climate scale, the organisational emotional intelligence questionnaire (OEIQ) and the Utrecht Work Engagement Scale (UWES). The data were analysed using hierarchical regression and partial least squares (PLS) analytical techniques. The results of the study show that both psychological climate and OEI, which represent emotional cognition of job, role, leader and organisation domains in the workplace are significant drivers of employee engagement. In particular, the study found that a sense of contribution and challenge at work are the strongest drivers of vigour, dedication and absorption and highlights the importance of emotionally relevant approaches in furthering our understanding of workplace engagement.

Keywords: employee engagement, organisational emotional intelligence, psychological climate, workplace attitudes

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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: German Galvez-Garcia, Javier Albayay, Javiera Peña, Marta Lavin, George A. Michael

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The aim of this research was to investigate whether the selection of the actions needs different inhibition processes during the response selection stage. In Experiment 1, we compared the magnitude of the Spatial Stroop effect, which occurs in response selection stage, in two motor actions (lifting vs reaching) when the participants performed both actions in the same block or in different blocks (mixed block vs. pure blocks).Within pure blocks, we obtained faster latencies when lifting actions were performed, but no differences in the magnitude of the Spatial Stroop effect were observed. Within mixed block, we obtained faster latencies as well as bigger-magnitude for Spatial Stroop effect when reaching actions were performed. We concluded that when no action selection is required (the pure blocks condition), inhibition works as a unitary system, whereas in the mixed block condition, where action selection is required, different inhibitory processes take place within a common processing stage. In Experiment 2, we investigated this common processing stage in depth by limiting participants’ available resources, requiring them to engage in a concurrent auditory task within a mixed block condition. The Spatial Stroop effect interacted with Movement as it did in Experiment 1, but it did not significantly interact with available resources (Auditory task x Spatial Stroop effect x Movement interaction). Thus, we concluded that available resources are distributed equally to both inhibition processes; this reinforces the likelihood of there being a common processing stage in which the different inhibitory processes take place.

Keywords: inhibition process, motor processes, selective inhibition, dual task

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Authors: Jee-Young Lee, Joaquim J. Ferreira, Hyeo-il Ma, José-Francisco Rocha, Beomseok Jeon

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The effective management of wearing-off is a key driver of medication changes for patients with Parkinson’s disease (PD) treated with levodopa (L-DOPA). While L-DOPA is well tolerated and efficacious, its clinical utility over time is often limited by the development of complications such as dyskinesia. Still, common first-line option includes adjusting the daily L-DOPA dose followed by adjunctive therapies usually counting for the L-DOPA equivalent daily dose (LEDD). The LEDD conversion formulae are a tool used to compare the equivalence of anti-PD medications. The aim of this work is to compare the effects of opicapone (OPC) 50 mg, a catechol-O-methyltransferase (COMT) inhibitor, and an additional 100 mg dose of L-DOPA in reducing the off time in PD patients with early motor fluctuations receiving different daily L-DOPA doses. OPC was found to be well tolerated and efficacious in advanced PD population. This work utilized patients' home diary data from a 4-week Phase 2 pharmacokinetics clinical study. The Korean ADOPTION study randomized (1:1) patients with PD and early motor fluctuations treated with up to 600 mg of L-DOPA given 3–4 times daily. The main endpoint was change from baseline in off time in the subgroup of patients receiving 300–400 mg/day L-DOPA at baseline plus OPC 50 mg and in the subgroup receiving >300 mg/day L-DOPA at baseline plus an additional dose of L-DOPA 100 mg. Of the 86 patients included in this subgroup analysis, 39 received OPC 50 mg and 47 L-DOPA 100 mg. At baseline, both L-DOPA total daily dose and LEDD were lower in the L-DOPA 300–400 mg/day plus OPC 50 mg group than in the L-DOPA >300 mg/day plus L-DOPA 100 mg. However, at Week 4, LEDD was similar between the two groups. The mean (±standard error) reduction in off time was approximately three-fold greater for the OPC 50 mg than for the L-DOPA 100 mg group, being -63.0 (14.6) minutes for patients treated with L-DOPA 300–400 mg/day plus OPC 50 mg, and -22.1 (9.3) minutes for those receiving L-DOPA >300 mg/day plus L-DOPA 100 mg. In conclusion, despite similar LEDD, OPC demonstrated a significantly greater reduction in off time when compared to an additional 100 mg L-DOPA dose. The effect of OPC appears to be LEDD independent, suggesting that caution should be exercised when employing LEDD to guide treatment decisions as this does not take into account the timing of each dose, onset, duration of therapeutic effect and individual responsiveness. Additionally, OPC could be used for keeping the L-DOPA dose as low as possible for as long as possible to avoid the development of motor complications which are a significant source of disability.

Keywords: opicapone, levodopa, pharmacokinetics, off-time

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Authors: Aldan A. Sapargaliyev, Yerbol A. Sapargaliyev

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Since the invention, the electric machine (EM) can be defined as oEM – one-vector electric machine, as it works due to one-vector inductive coupling with use of one-vector electromagnet. The disadvantages of oEM are large size and limited efficiency at low and medium power applications. This paper describes multi-vector electric machine (mEM) based on multi-vector inductive coupling, which is characterized by the increased surface area of ​​the inductive coupling per EM volume, with a reduced share of inefficient and energy-consuming part of the winding, in comparison with oEM’s. Particularly, it is considered, calculated and compared the performance of three different electrical motors and their power at the same volumes and rotor frequencies. It is also presented the result of calculation of correlation between power density and volume for oEM and mEM. The method of multi-vector inductive coupling enables mEM to possess 1.5-4.0 greater density of power per volume and significantly higher efficiency, in comparison with today’s oEM, especially in low and medium power applications. mEM has distinct advantages, when used in transport vehicles such as electric cars and aircrafts.

Keywords: electric machine, electric motor, electromagnet, efficiency of electric motor

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