Journal Description
Applied System Innovation
Applied System Innovation
is an international, peer-reviewed, open access journal on integrated engineering and technology. The journal is owned by the International Institute of Knowledge Innovation and Invention (IIKII) and is published bimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and other databases.
- Journal Rank: CiteScore - Q1 (Applied Mathematics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.7 days after submission; acceptance to publication is undertaken in 4.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.8 (2022);
5-Year Impact Factor:
3.0 (2022)
Latest Articles
Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
Appl. Syst. Innov. 2024, 7(1), 8; https://doi.org/10.3390/asi7010008 - 08 Jan 2024
Abstract
The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is
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The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER models have limited semantic extraction capabilities and cannot capture deep semantic information between nested entities in the text. To address these issues, this paper proposes a model that uses the GlobalPointer module to identify nested entities in the text and constructs the IDCNNLR semantic extraction module to extract deep semantic information. Furthermore, multiple-head self-attention mechanisms are incorporated into the model at multiple positions to achieve data denoising, enhancing the quality of semantic features. The proposed model considers each possible entity boundary through the GlobalPointer module, and the IDCNNLR semantic extraction module and multi-position attention mechanism are introduced to enhance the model’s semantic extraction capability. Experimental results demonstrate that the proposed model achieves F1 scores of 69.617% and 79.285% on the CMeEE Chinese nested entity recognition dataset and CLUENER2020 Chinese fine-grained entity recognition dataset, respectively. The model exhibits improvement compared to baseline models, and each innovation point shows effective performance enhancement in ablative experiments.
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(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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Broader Terms Curriculum Mapping: Using Natural Language Processing and Visual-Supported Communication to Create Representative Program Planning Experiences
Appl. Syst. Innov. 2024, 7(1), 7; https://doi.org/10.3390/asi7010007 - 31 Dec 2023
Abstract
Accreditation bodies call for curriculum development processes that are open to all stakeholders, reflecting viewpoints of students, industry, university faculty, and society. However, communication difficulties between faculty and non-faculty groups leave an immense collaboration potential unexplored. Using the classification of learning objectives, natural
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Accreditation bodies call for curriculum development processes that are open to all stakeholders, reflecting viewpoints of students, industry, university faculty, and society. However, communication difficulties between faculty and non-faculty groups leave an immense collaboration potential unexplored. Using the classification of learning objectives, natural language processing, and data visualization, this paper presents a quantitative method that delivers program plan representations that are universal, self-explanatory, and empowering; promoting stronger links between program courses and curriculum development open to all stakeholders. A simple example shows how the method contributes to representative program planning experiences and a case study is used to confirm the method’s accuracy and utility.
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(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)
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AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests
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Appl. Syst. Innov. 2024, 7(1), 6; https://doi.org/10.3390/asi7010006 - 28 Dec 2023
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Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and
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Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.
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(This article belongs to the Section Applied Systems on Educational Innovations and Emerging Technologies)
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Aerodynamic Drag Study of the Heat Exchange Equipment with Different Fin Geometries
Appl. Syst. Innov. 2024, 7(1), 5; https://doi.org/10.3390/asi7010005 - 27 Dec 2023
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This article is devoted to the method of numerical modelling of aerodynamics when the air flows around fins of a special design, which is implemented in SolidWorks Flow Simulation. The study was carried out for three types of rib orientation, and the aerodynamic
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This article is devoted to the method of numerical modelling of aerodynamics when the air flows around fins of a special design, which is implemented in SolidWorks Flow Simulation. The study was carried out for three types of rib orientation, and the aerodynamic drag coefficients were determined for different values of the Reynolds number. It was confirmed that the drag coefficient values depend significantly on the flow regime. The lowest value of the drag coefficient is observed when the fins are oriented from a larger diameter to a smaller one. In the laminar regime (Re < 2300), the average value of CX = 1.04, in the transitional regime (2300 < Re < 10,000), CX = 0.74, and in the turbulent regime (Re > 10,000), CX = 0.22. Characteristic for this case of orientation is a significant decrease in the drag coefficient during the transition from laminar to turbulent regime; the minimum is observed at the flow speed in the range between 2 and 3 m/s.
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Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach
Appl. Syst. Innov. 2024, 7(1), 4; https://doi.org/10.3390/asi7010004 - 25 Dec 2023
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The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require
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The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require a high computational cost due to the involvement of a large number of coupled differential equations modeling electron and photon behavior. In this paper, we model the general LMI process involving an electromagnetic interaction medium and optical (light) excitation in one dimension (1D) via the use of a dynamic deep learning algorithm where the neural network coefficients can precisely adapt themselves based on the past values of the coefficients of adjacent layers even under the availability of very limited data. Due to the high computational cost of LMI simulations, simulation data are usually only available for short durations. Our aim here is to implement an adaptive deep learning-based model of the LMI process in 1D based on available temporal data so that the electromagnetic features of LMI simulations can be quickly decrypted by the evolving network coefficients, facilitating self-learning. This enables accurate prediction and acceleration of LMI simulations that can run for much longer durations via the reduction in the cost of computation through the elimination of the requirement for the simultaneous computation and discretization of a large set of coupled differential equations at each simulation step. Our analyses show that the LMI process can be efficiently decrypted using dynamic deep learning with less than 1% relative error (RE), enabling the extension of LMI simulations using simple artificial neural networks.
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(This article belongs to the Section Applied Mathematics)
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Using Smart Traffic Lights to Reduce CO2 Emissions and Improve Traffic Flow at Intersections: Simulation of an Intersection in a Small Portuguese City
Appl. Syst. Innov. 2024, 7(1), 3; https://doi.org/10.3390/asi7010003 - 25 Dec 2023
Abstract
Reducing CO2 emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method
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Reducing CO2 emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO2 emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO2 emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions.
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(This article belongs to the Section Control and Systems Engineering)
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Project Management Efficiency Measurement with Data Envelopment Analysis: A Case in a Petrochemical Company
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Appl. Syst. Innov. 2024, 7(1), 2; https://doi.org/10.3390/asi7010002 - 22 Dec 2023
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The research question this study poses is how to measure the efficiency of project management activities. The purpose of this article is to quantify the efficiency of the execution of a project portfolio managed by a project management office (PMO) structure. The research
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The research question this study poses is how to measure the efficiency of project management activities. The purpose of this article is to quantify the efficiency of the execution of a project portfolio managed by a project management office (PMO) structure. The research subject is a PMO operating within a petrochemical manufacturing company in southern Brazil. The research method is quantitative modeling. The study employed data envelopment analysis (DEA) to calculate the relative efficiencies of projects in three classes according to complexity over a period of four years. Each project is a decision-making unit (DMU), as required by the DEA procedure. One novelty is the calculation of cost- and time-weighted efficiency values, which slightly differ from the average. The main results indicate that the average efficiency for classes of projects roughly stands between 40 and 80%. The results also indicate a learning process guided by the PMO, as the average efficiency increased over three years in two classes of projects, according to the prioritization imposed by the office. The study also pointed out that the most influential variables in determining project efficiency are accuracy in meeting deadlines and the time planned for completion. The most important implication is that, from now on, the company has a theoretical foundation to justify focusing further efforts on reducing and controlling time to completion, not only cost and scope conformity, to increase overall project efficiency. Future research should prioritize investigating management techniques that increase the likelihood of completing projects within their deadlines.
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Dynamic Queries through Augmented Reality for Intelligent Video Systems
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Appl. Syst. Innov. 2024, 7(1), 1; https://doi.org/10.3390/asi7010001 - 19 Dec 2023
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Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is
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Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is to obtain information from large areas, it can become complicated if it is necessary to track an object of interest, such as people or vehicles, due to the vision space that a single camera can cover; this opens the way to distributed zone monitoring systems made up of a set of cameras that aim to cover a larger area. Distributed zone monitoring systems add great versatility, becoming more complex in terms of the complexity of information analysis, communication, interoperability, and heterogeneity in the interpretation of information. In the literature, the development of distributed schemes has focused on representing data communication and sharing challenges. Currently, there are no specific criteria for information exchange and analysis in a distributed system; hence, different models and architectures have been proposed. In this work, the authors present a framework to provide homogeneity in a distributed monitoring system. The information is obtained from different cameras, where a global reference system is defined for generated trajectories, which are mapped independently of the model used to obtain the dynamics of the movement of people within the vision area of a distributed system, thus allowing for its use in works where there is a large amount of information from heterogeneous sources. Furthermore, we propose a novel similarity metric that allows for information queries from heterogeneous sources. Finally, to evaluate the proposed performance, the authors developed several distributed query applications in an augmented reality system based on realistic environments and historical data retrieval using a client–server model.
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(This article belongs to the Section Artificial Intelligence)
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Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study
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Appl. Syst. Innov. 2023, 6(6), 115; https://doi.org/10.3390/asi6060115 - 30 Nov 2023
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This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are
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This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model’s performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.
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Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
Appl. Syst. Innov. 2023, 6(6), 114; https://doi.org/10.3390/asi6060114 - 30 Nov 2023
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While digital twins (DTs) have achieved significant visibility, they continue to face a problem of lack of harmonisation regarding their interpretation and definition. This diverse and interchangeable use of terms makes it challenging for scientific activities to take place and for organisations to
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While digital twins (DTs) have achieved significant visibility, they continue to face a problem of lack of harmonisation regarding their interpretation and definition. This diverse and interchangeable use of terms makes it challenging for scientific activities to take place and for organisations to grasp the existing opportunities and how can these benefit their businesses. This article aims to shift the focus away from debating a definition for a DT. Instead, it proposes a conceptual approach to the digital twinning of engineering physical assets as an ongoing process with variable complexity and evolutionary capacity over time. To accomplish this, the article presents a functional architecture of digital twinning, grounded in the foundational elements of the DT, to reflect the various forms and levels of digital twinning (LoDT) of physical assets throughout their life cycles. Furthermore, this work presents UNI-TWIN—a unified model to assist organisations in assessing the LoDT of their assets and to support investment planning decisions. Three case studies from the road and rail sector validate its applicability. UNI-TWIN helps to redirect the discussion around DTs and emphasise the opportunities and challenges presented by the diverse realities of digital twinning, namely in the context of engineering asset management.
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(This article belongs to the Collection Feature Paper Collection on Civil Engineering and Architecture)
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Adaptive Learning in Agent-Based Models: An Approach for Analyzing Human Behavior in Pandemic Crowding
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Appl. Syst. Innov. 2023, 6(6), 113; https://doi.org/10.3390/asi6060113 - 29 Nov 2023
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This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model simulating behavior on a university campus during a pandemic outbreak, with the particular case of the COVID-19 pandemic. Our model not only captures individual behavior, but also serves
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This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model simulating behavior on a university campus during a pandemic outbreak, with the particular case of the COVID-19 pandemic. Our model not only captures individual behavior, but also serves as a powerful tool for assessing the efficacy of geolocalized policies in addressing campus overcrowding and infections. The main objective is to demonstrate RL’s effectiveness in representing agent behavior and optimizing control policies through adaptive decision-making in response to evolving pandemic dynamics. By implementing RL, we identify different temporal patterns of overcrowding violations, shedding light on the complexity of human behavior within semi-enclosed environments. While we successfully reduce campus overcrowding, the study recognizes its limited impact on altering the pandemic’s course, underlining the importance of comprehensive epidemic control strategies. Our research contributes to the understanding of adaptive learning in complex systems and provides insights for shaping future public health policies in similar community settings. It emphasizes the significance of considering individual decision-making influenced by adaptive learning, implementing targeted interventions, and the role of geospatial elements in pandemic control. Future research directions include exploring various parameter settings and updating representations of the disease’s natural history to enhance the applicability of these findings. This study offers valuable insights into managing pandemics in community settings and highlights the need for multifaceted control strategies.
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IntelliTrace: Intelligent Contact Tracing Method Based on Transmission Characteristics of Infectious Disease
Appl. Syst. Innov. 2023, 6(6), 112; https://doi.org/10.3390/asi6060112 - 23 Nov 2023
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The COVID-19 pandemic has underscored the necessity for rapid contact tracing as a means to effectively suppress the spread of infectious diseases. Existing contact tracing methods leverage location-based or distance-based detection to identify contact with a confirmed patient. Existing contact tracing methods have
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The COVID-19 pandemic has underscored the necessity for rapid contact tracing as a means to effectively suppress the spread of infectious diseases. Existing contact tracing methods leverage location-based or distance-based detection to identify contact with a confirmed patient. Existing contact tracing methods have encountered challenges in practical applications, stemming from the tendency to classify even casual contacts, which carry a low risk of infection, as close contacts. This issue arises because the transmission characteristics of the virus have not been fully considered. This study addresses the above problem by proposing IntelliTrace, an intelligent method that introduces methodological innovations prioritizing shared environmental context over physical proximity. This approach more accurately assesses potential transmission events by considering the transmission characteristics of the virus, with a special focus on COVID-19. In this study, we present space-based indoor Wi-Fi contact tracing using machine learning for indoor environments and trajectory-based outdoor GPS contact tracing for outdoor environments. For an indoor environment, a contact is detected based on whether users are in the same space with the confirmed case. For an outdoor environment, we detect contact through judgments based on the companion statuses of people, such as the same movements in their trajectories. The datasets obtained from 28 participants who installed the smartphone application during a one-month experiment in a campus space were utilized to train and validate the performance of the proposed exposure-detection method. As a result of the experiment, IntelliTrace exhibited an F1 score performance of 86.84% in indoor environments and 94.94% in outdoor environments.
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(This article belongs to the Section Information Systems)
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Physical Modelling of the Set of Performance Curves for Radial Centrifugal Pumps to Determine the Flow Rate
Appl. Syst. Innov. 2023, 6(6), 111; https://doi.org/10.3390/asi6060111 - 17 Nov 2023
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To depict the pump power characteristics of radial centrifugal pumps, a physical model was developed. The model relies on established empirical equations. To parameterize the model for specific pumps, physically interpretable tuning factors were integrated. The tuning factors are identified by using the
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To depict the pump power characteristics of radial centrifugal pumps, a physical model was developed. The model relies on established empirical equations. To parameterize the model for specific pumps, physically interpretable tuning factors were integrated. The tuning factors are identified by using the Levenberg–Marquardt method, which was applied to the characteristic curve at a constant speed. A cross-validation of the physical model highlighted the advantage of representing the set of performance curves with less deviation compared to approximation functions. Calculating the entire set of performance curves requires only one pump characteristic curve at a constant speed. Therefore, only a single measurement is necessary. Furthermore, the physical model can be used to calculate the changes in the set of performance curves due to prewhirl. This increases the accuracy of flow rate calculations when prewhirl occurs.
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(This article belongs to the Section Industrial and Manufacturing Engineering)
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Unlocking Potential: The Development and User-Friendly Evaluation of a Virtual Reality Intervention for Attention-Deficit/Hyperactivity Disorder
Appl. Syst. Innov. 2023, 6(6), 110; https://doi.org/10.3390/asi6060110 - 16 Nov 2023
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(1) Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is typically first diagnosed in early childhood. Medication and cognitive behavioural therapy are considered effective in treating children with ADHD, whereas these treatments appear to have some side effects and restrictions. Virtual reality (VR), therefore, has been applied
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(1) Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is typically first diagnosed in early childhood. Medication and cognitive behavioural therapy are considered effective in treating children with ADHD, whereas these treatments appear to have some side effects and restrictions. Virtual reality (VR), therefore, has been applied to exposure therapy for mental disorders. Previous studies have adopted VR in the cognitive behavioural treatment for children with ADHD; however, no research has used VR to develop social skills training for children with ADHD. This study aimed to develop a VR-based intervention (Social VR) to improve social skills in children with symptoms of ADHD. Prior to conducting the pilot trial to assess the effectiveness of Social VR, valuable user feedback was gathered regarding the mechanics of Social VR, satisfaction and motion sickness. This study presented the development and preliminary usability of Social VR to enhance social interaction skills among children with ADHD. (2) Methods: The development process of the Social VR intervention was demonstrated. The Social VR intervention consisted of three scenarios, namely MTR, Campus and Market and Restaurant. In the usability study, 25 children with ADHD were recruited to test the Social VR during the preliminary usability stage of a clinical trial at preinclusion. The participants completed a survey about their experience of playing Social VR, satisfaction, and motion sickness. (3) Results: The participants indicated the three conditions had easy-to-follow instructions, were easy to pick up, and that they understood when the situations changed. The control and beauty of the graphics of Market and Restaurant were relatively lower compared with those of MTR and Campus. The three scenarios are applicable to children diagnosed with any subtype of ADHD. (4) Conclusion: The participants were satisfied with Social VR. Social VR was generally considered realistic and immersive. Further trials to assess the feasibility and efficacy were discussed. If proven effective, VR-based intervention can be an adjunctive approach to current multimodal training for children with ADHD.
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(This article belongs to the Section Artificial Intelligence)
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Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
Appl. Syst. Innov. 2023, 6(6), 109; https://doi.org/10.3390/asi6060109 - 14 Nov 2023
Abstract
Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional
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Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.
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(This article belongs to the Section Artificial Intelligence)
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Simulating the Software Development Lifecycle: The Waterfall Model
Appl. Syst. Innov. 2023, 6(6), 108; https://doi.org/10.3390/asi6060108 - 14 Nov 2023
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This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python
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This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.
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Assessment of Batteries’ Contribution for Optimal Self-Sufficiency in Large Building Complexes
Appl. Syst. Innov. 2023, 6(6), 107; https://doi.org/10.3390/asi6060107 - 14 Nov 2023
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The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the
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The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the same climate goals. In order to achieve these goals, ensuring a consistent supply and the effective use of clean energy is pursued, as it has a significant impact on the sustainable development and growth of the country. As the Greek tourism sector is one of the most energy-consuming of the national economy and a major contributor to the country’s GDP, opportunities are presented for innovation and investment in sustainable practices. Such investments must focus on buildings and facilities, where the energy consumption is concentrated. One of the most popular holiday destinations in Greece is the island of Crete. Visitation patterns are seasonal, which means during the summer months, Crete is exceptionally popular and more demanding energy-wise. One of the highest energy-demanding types of tourism-based businesses is the hospitality industry. Energy demands in hotels are driven by factors such as heating, cooling, lighting, and hot water. Thus, such activities require thermal and electrical energy to function. Electrical energy is one of the most essential forms of energy for hotels, as it powers a wide range of critical systems and services throughout the establishment. Therefore, the hotels are highly susceptible to fluctuations in energy prices which can significantly impact the operational costs of hotels. This paper presents an analysis of the annual consumption for the year of 2022 of five hotels located in Crete. An algorithm is also implemented which strives to minimize the capital expenditure (CAPEX), while ensuring a sufficient percentage of self-sufficiency.
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(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
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Stock Market Prediction Using Deep Reinforcement Learning
Appl. Syst. Innov. 2023, 6(6), 106; https://doi.org/10.3390/asi6060106 - 10 Nov 2023
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Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this
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Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.
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(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)
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Open AccessArticle
Application of Segmented and Prestressed Supporting Structures in Bridge Crane Systems: Potentials and Challenges
by
and
Appl. Syst. Innov. 2023, 6(6), 105; https://doi.org/10.3390/asi6060105 - 09 Nov 2023
Abstract
In this paper, an alternative design approach to the construction of bridge crane systems is analyzed with respect to the potentials and challenges of use based on two possible construction methods. Compared to conventional crane bridges, which are manufactured as a single part,
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In this paper, an alternative design approach to the construction of bridge crane systems is analyzed with respect to the potentials and challenges of use based on two possible construction methods. Compared to conventional crane bridges, which are manufactured as a single part, the innovation of the approach relates to designing the crane bridge in segments and assembling it from standardized individual components, which are small in dimension, to form a plug-in structure. These are then prestressed by means of a tensile member to establish the load-bearing capacity. The motivation of the alternative design concept arises from a challenging manufacturing and costly transportation of common crane bridges. Here, the different design approaches are first presented as to how a segmental crane bridge can be constructed and which function the involved components fulfill. In this context, the novel construction method also gives rise to new constraints that are not covered by the common design standards and are therefore discussed. The paper concludes with a comparative study to identify advantages and disadvantages of both concepts regarding defined criteria with the aim of determining design improvements and elaborates the potentials and challenges of the approach with respect to a future industrial implementation. Moreover, these findings additionally form the basis for further research work in this area.
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(This article belongs to the Section Industrial and Manufacturing Engineering)
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Numerical Investigations and Artificial Neural Network-Based Performance Prediction of a Centrifugal Fan Having Innovative Hub Geometry Designs
Appl. Syst. Innov. 2023, 6(6), 104; https://doi.org/10.3390/asi6060104 - 06 Nov 2023
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It is a well-known fact that air approaches the eye region of the rotating impeller of a centrifugal fan with shock-less entry conditions in an ideal scenario. The flow in this region is associated with induced swirl losses, leading to cumulative performance losses.
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It is a well-known fact that air approaches the eye region of the rotating impeller of a centrifugal fan with shock-less entry conditions in an ideal scenario. The flow in this region is associated with induced swirl losses, leading to cumulative performance losses. Proper flow guidance in the vicinity of the eye region is essential to minimize possible flow losses. The flow guiding structure may be in the form of a projection or extrusion connected to the rotating impeller of the turbo machines and is generally named a hub. These attachments enhance the overall flow augmentation of the turbo machines in terms of static pressure improvement by reducing a significant amount of inlet turning losses. This article attempts to highlight the efficacy of hubs of various shapes and sizes on the pressure rise of the centrifugal fan using Computational Fluid Dynamics (CFD). Simulation results revealed that the optimized hub configuration yields about an 8.4% higher head coefficient and 8.6% higher relative theoretical efficiency than that obtained for the hub-less base configuration. This improvement in these paraments therefore also commemorates the global progress in energy efficiency as per the UN’s Sustainable Development Goals, SDG 7 in particular. Simultaneously, in the Artificial Neural Network (ANN), a Multi-Layer Perceptron (MLP) model is used to forecast the performance of a centrifugal fan with an optimized hub design. The results predicted by the ANN model are found to be in close agreement with the optimized hub shape’s numerical results.
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