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Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction
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Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone.
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In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components
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In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components that constitute this innovative system, we explore its fundamental architecture and how each element contributes to seamless information flow. The benefits of adopting a medical information system are highlighted, emphasizing improved patient care, streamlined processes, and enhanced decision making for healthcare professionals.
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This article is about experiments investigating teaching and learning processes and their effects on students. Specifically, the laboratory experiment method aims to determine if using virtual reality in classes leads to better learning outcomes, knowledge retention, satisfaction, engagement, and attractiveness compared to traditional
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This article is about experiments investigating teaching and learning processes and their effects on students. Specifically, the laboratory experiment method aims to determine if using virtual reality in classes leads to better learning outcomes, knowledge retention, satisfaction, engagement, and attractiveness compared to traditional teaching methods. The study found that students who used VR (Experimental Group—EG) had significantly better learning outcomes (with an average of 5.9747) compared to the control group (Control Group—CG), who only had traditional classes (with an average of 4.6229). The study employed a Likert scale from 1 to 7. The difference between EG and CG was 29.2%. Furthermore, the study found that students in the EG had higher knowledge retention, satisfaction, engagement, and attractiveness compared to the CG. All measurements were above 6.4 on the same scale. This study is important because it explores innovative teaching methods and their potential to improve learning outcomes, satisfaction, and efficiency. It also opens up avenues for further research on teaching methodologies for undergraduate students.
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This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet,
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This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.
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This study examines the global literature that looks at spatial–visual abilities (SVA) while considering the numerous differential studies, methods of evaluation designed over a century, and multiple external influences on its development. The dataset was retrieved from Google Scholar and publisher databases such
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This study examines the global literature that looks at spatial–visual abilities (SVA) while considering the numerous differential studies, methods of evaluation designed over a century, and multiple external influences on its development. The dataset was retrieved from Google Scholar and publisher databases such as Elsevier, Taylor & Francis, Springer, etc. Only factual reports and bibliographic reviews were included in an analysis of a total of 87 documents. Each study of SVA is classified based on information, country, year, and age groupings. SVA has been extensively studied in the areas of “STEM (Science, Technology, Engineering and Mathematics) fields”, “demographic factors” and “other activities”. “Spatial visualisation” or “visual ability” is the term employed to refer to the cognitive ability that allows one to comprehend, mentally process, and manipulate three-dimensional visuospatial shapes. One of the most crucial distinct abilities involved is spatial aptitude, which aids in understanding numerous aspects of everyday and academic life. It is especially vital for comprehending scientific concepts, and it has been extensively studied. Nearly all multiple-aptitude assessments include spatial ability. It is determined that over the past two decades, the study of SVA has gained momentum, most likely because of information being digitised. Within the vast reservoir of spatial-cognition research, the majority of the studies examined here originate from the United States of America, with less than a quarter of the studies based in the Asia–Pacific region and the Middle East. This paper presents a comprehensive review of the literature on the assessment of SVA with respect to sector, year, country, age and socio-economic factors. It also offers a detailed examination of the use of spatial interventions in educational environments to integrate spatial abilities with training in architecture and interior design.
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Abstract: This research addresses the critical gap in enabling effective coopetition networks through technological innovation with the development of Cockpit4.0+, an Industrial Internet of Things (IIoT) artefact tailored for small- and medium-sized enterprises (SMEs). By employing the principles of Service-Dominant Logic (S-D Logic)
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Abstract: This research addresses the critical gap in enabling effective coopetition networks through technological innovation with the development of Cockpit4.0+, an Industrial Internet of Things (IIoT) artefact tailored for small- and medium-sized enterprises (SMEs). By employing the principles of Service-Dominant Logic (S-D Logic) and leveraging the Design Science Research (DSR) methodology, Cockpit4.0+ represents a pioneering approach to incorporating the IIoT within ecosystems for value co-creation. This facilitates competition and cooperation among firms, enhancing the operational dynamics within SME networks. Evaluated by experts in the ornamental stone sector, a significant sector of the Portuguese economy, the system demonstrated a positive functional acceptance rate of 78.9%. An experimental test was conducted following the positive preliminary functional evaluation of Cockpit4.0+, especially among more digitally advanced companies. The findings revealed that the on-time delivery performance under current best practices (CB.Ps) was 67.1%. In contrast, implementing coopetition network practices (CN.Ps) increased on-time delivery to 77.5%. These positive evaluations of Cockpit4.0+ underscore the practical applicability of S-D Logic and provide fresh insights into the dynamics of coopetition, particularly beneficial for SMEs. Despite its promising results, the real-world efficacy of IIoT systems like Cockpit4.0+ requires further empirical studies to verify these findings. Future research should focus on examining the scalability of Cockpit4.0+ and its adaptability across various sectors and enhancing its cybersecurity measures to ensure its long-term success and broader adoption.
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Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this
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Total real and reactive power losses in electrical power systems are an inevitable phenomenon and occur due to several factors, such as conductor resistance, transformer impedance, line reactance, equipment losses, and phase unbalance. Minimizing them is crucial to the system’s efficiency. In this study, an artificial neural network, specifically a Multi-layer Perceptron, was employed to predict total real and reactive power losses in electrical systems. The network is composed of three layers: an input layer consisting of the variables loading factor, real and reactive power generated on the slack bus, a hidden layer, and an output layer representing the total real and reactive power losses. The training method used was backpropagation, adjusting the weights based on the desired output. The results obtained, using datasets from IEEE systems with 14, 30, and 57 buses, showed satisfactory performance, with a mean squared error of around 10−4 and a coefficient of determination (R2) of 0.998. In validation with 20% of the data that was not part of the training, the network demonstrated effectiveness, with a mean squared error around 10−3. This indicates that the network was able to accurately predict total power losses based on loads, generating estimates close to the desired values.
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Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To
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Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To further our understanding of this topic, we surveyed authentication schemes, particularly systems and methods deployed in XR settings. In this survey, we focused on reviewing and evaluating papers published during the last decade (between 2014 and 2023). We compared knowledge-based authentication, physical biometrics, behavioral biometrics, and multi-model methods in terms of accuracy, security, and usability. We also highlighted the benefits and drawbacks of those methods. These highlights will direct future Human–computer Interaction (HCI) and security research to develop secure, reliable, and practical authentication systems.
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In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects
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In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships.
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This paper outlines the numerical modeling procedure aimed at defining the guidelines for the development of a continuous microwave-assisted pilot plant for shelled almond disinfestation, as an alternative to the use of chemicals. To this end, a 3D Multiphysics numerical tool involving both
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This paper outlines the numerical modeling procedure aimed at defining the guidelines for the development of a continuous microwave-assisted pilot plant for shelled almond disinfestation, as an alternative to the use of chemicals. To this end, a 3D Multiphysics numerical tool involving both electromagnetic and thermal models was developed to predict the temperature and electric field profiles inside the microwave treatment chamber. Three different microwave sources arrangements were simulated and the accuracy of the model was verified under different residence times of almonds in the treatment chamber using the developed prototype. The modeling results demonstrated that the arrangement having five microwave sources, each delivering a maximum power of 1.5 kW and frequency of 2.45 GHz, ensures good heating uniformity. The obtained results proved that the model enables the accurate prediction of the temperature trend (root-mean-square error/RMSE = 0.82). A strong linear regression was detected for the standard deviation between the simulated and experimental data (linear regression, R2 = 0.91). The very low COV value for the experimental temperature data demonstrated the heating uniformity as the treatment time changed. The developed model and the simulation strategy used may provide useful design guidance for microwave-assisted continuous plants for disinfestation, with a significant impact on the almond industry.
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Social robots are being conceived with different characteristics and being used in different applications. The growth of social robotics benefits from advances in fabrication, sensing, and actuation technologies, as well as signal processing and artificial intelligence. This paper presents a design and implementation
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Social robots are being conceived with different characteristics and being used in different applications. The growth of social robotics benefits from advances in fabrication, sensing, and actuation technologies, as well as signal processing and artificial intelligence. This paper presents a design and implementation of the humanoid robotic platform Adam, consisting of a motorized human-like head with precise movements of the eyes, jaw, and neck, together with capabilities of face tracking and vocal conversation using ChatGPT. Adam relies on 3D-printed parts together with a microphone, a camera, and proper servomotors, and it has high structural integrity and flexibility. Adam’s control framework consists of an adequate signal exploitation and motor command strategy that allows efficient social interactions. Adam is an innovative platform that combines manufacturability, user-friendliness, low costs, acceptability, and sustainability, offering advantages compared with other platforms. Indeed, the platform’s hardware and software components are adjustable and allow it to increase its abilities and adapt them to different applications in a variety of roles. Future work will entail the development of a body for Adam and the addition of skin-like materials to enhance its human-like appearance.
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Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating
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Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers.
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