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Value of the 3D Product Model Use in Assembly Processes: Process Planning, Design, and Shop Floor Execution -
Chances and Risks of Artificial Intelligence—A Concept of Developing and Exploiting Machine Intelligence for Future Societies -
Digital Twin: Origin to Future -
Assessment of Several Approaches to Biofortified Products: A Literature Review -
Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
Journal Description
Applied System Innovation
Applied System Innovation
is an international, peer-reviewed, open access journal on integrated engineering and technology, published quarterly online by MDPI.
- Open Access— free for reader, free-publication for well-prepared manuscripts submitted before 31 December 2021.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and many other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 14.1 days after submission; acceptance to publication is undertaken in 5.7 days (median values for papers published in this journal in the first half of 2021).
- 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.
Latest Articles
A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
Appl. Syst. Innov. 2021, 4(3), 58; https://doi.org/10.3390/asi4030058 - 27 Aug 2021
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In this article, a method for the efficient charging of electric vehicles (EVs) at the parking lot (PL) level, including V2G operation and taking into account lifetime of EV batteries, distribution network and local transformer loading, is proposed. The main targets of the
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In this article, a method for the efficient charging of electric vehicles (EVs) at the parking lot (PL) level, including V2G operation and taking into account lifetime of EV batteries, distribution network and local transformer loading, is proposed. The main targets of the method are to minimize the total charging cost of the PLs hosting the EVs and to satisfy all technical and operation constraints of EVs and PLs. The proposed method exploits particle swarm optimization (PSO) to derive the charging schedule of the EVs. The proposed method is compared with conventional charging strategies, where the EVs are charged with the maximum power of their charging power converter or the average power required to achieve their state-of-charge target, and a conventional charging scheduling method using the aggregated behavior of the plug-in EVs. Real-world data series of electricity price and parking lot activity were used. The results obtained from the study of indicative operation scenarios prove the effectiveness of the proposed method, while no sophisticated computing, measurement and communication systems are required for its application.
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Open AccessArticle
Piezoelectric and Electromechanical Characteristics of Porous Poly(Ethylene-co-Vinyl Acetate) Copolymer Films for Smart Sensors and Mechanical Energy Harvesting Applications
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Appl. Syst. Innov. 2021, 4(3), 57; https://doi.org/10.3390/asi4030057 - 26 Aug 2021
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This paper investigates energy harvesting performances of porous piezoelectric polymer films to collect electrical energy from vibrations and power various sensors. The influence of void content on the elastic matrix, dielectric, electrical, and mechanical properties of porous piezoelectric polymer films produced from available
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This paper investigates energy harvesting performances of porous piezoelectric polymer films to collect electrical energy from vibrations and power various sensors. The influence of void content on the elastic matrix, dielectric, electrical, and mechanical properties of porous piezoelectric polymer films produced from available commercial poly(ethylene-co-vinyl acetate) using an industrially applicable melt-state extrusion method (EVA) were examined and discussed. Electrical and mechanical characterization showed an increase in the harvested current and a decrease in Young’s modulus with the increasing ratio of voids. Thermal analysis revealed a decrease in piezoelectric constant of the porous materials. The authors present a mathematical model that is able to predict harvested current as a function of matrix characteristics, mechanical excitation and porosity percentage. The output current is directly proportional to the porosity percentage. The harvested power significantly increases with increasing strain or porosity, achieving a power value up to 0.23, 1.55, and 3.87 mW/m3 for three EVA compositions: EVA 0%, EVA 37% and EVA 65%, respectively. In conclusion, porous piezoelectric EVA films has great potential from an energy density viewpoint and could represent interesting candidates for energy harvesting applications. Our work contributes to the development of smart materials, with potential uses as innovative harvester systems of energy generated by different vibration sources such as roads, machines and oceans.
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Open AccessArticle
Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
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Appl. Syst. Innov. 2021, 4(3), 56; https://doi.org/10.3390/asi4030056 - 21 Aug 2021
Abstract
The widespread adoption of wearables, extended reality, and metaverses has accelerated the diverse configurations of remote collaboration and telementoring systems. This paper explores the opportunities and challenges of interactive telementoring, especially for wearers of smartglasses. In particular, recent relevant studies are reviewed to
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The widespread adoption of wearables, extended reality, and metaverses has accelerated the diverse configurations of remote collaboration and telementoring systems. This paper explores the opportunities and challenges of interactive telementoring, especially for wearers of smartglasses. In particular, recent relevant studies are reviewed to derive the needs and trends of telementoring technology. Based on this analysis, we define what can be integrated into smartglass-enabled interactive telementoring. To further illustrate this type of special use case for telementoring, we present five illustrative and descriptive scenarios. We expect our specialized use case to support various telementoring applications beyond medical and surgical telementoring, while harmoniously fostering cooperation using the smart devices of mentors and mentees at different scales for collocated, distributed, and remote collaboration.
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(This article belongs to the Special Issue Advanced Virtual Reality Technologies and Their Applications)
Open AccessCommunication
Lean-ing Method in an Emergency Department of the Italian Epicenter of the COVID-19 Outbreak: When the Algorithm Makes Difference
Appl. Syst. Innov. 2021, 4(3), 55; https://doi.org/10.3390/asi4030055 - 12 Aug 2021
Abstract
The Lean method entails a set of standardized processes intending to optimize resources, reduce waste, and improve results. Lean has been proposed as an operative model for the COVID-19 outbreak. Herein, we summarized data resulted from the Lean model adoption in an Emergency
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The Lean method entails a set of standardized processes intending to optimize resources, reduce waste, and improve results. Lean has been proposed as an operative model for the COVID-19 outbreak. Herein, we summarized data resulted from the Lean model adoption in an Emergency Department of the Lombardy region, the Italian epicenter of the pandemic, to critically appraise its effectiveness and feasibility. The Lean algorithm was applied in the Humanitas Clinical and Research Hospital, Milan, north of Italy. At admission, patients underwent outdoor pre-triage for fever, respiratory, and gastrointestinal symptoms, with a focus on SpO2. Based on these data, they were directed to the most appropriate area for the COVID-19 first-level screening. High-risk patients were assisted by trained staff for second-level screening and planning of treatment. Out of 7.778 patients, 21.9% were suspected of SARS-CoV-2 infection. Mortality was 21.9% and the infection rate in health workers was 4.8%. The lean model has proved to be effective in optimizing the overall management of COVID-19 patients in an emergency setting. It allowed for screening of a large volume of patients, while also limiting the health workers’ infection rate. Further studies are necessary to validate the suggested approach.
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(This article belongs to the Special Issue Systems and Industries in Response to COVID-19 Crisis)
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Open AccessArticle
The Role of Correlation in the Performance of Massive MIMO Systems
Appl. Syst. Innov. 2021, 4(3), 54; https://doi.org/10.3390/asi4030054 - 12 Aug 2021
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Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly
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Massive multiple-input multiple-output (m-MIMO) is considered as an essential technique to meet the high data rate requirements of future sixth generation (6G) wireless communications networks. The vast majority of m-MIMO research has assumed that the channels are uncorrelated. However, this assumption seems highly idealistic. Therefore, this study investigates the m-MIMO performance when the channels are correlated and the base station employs different antenna array topologies, namely the uniform linear array (ULA) and uniform rectangular array (URA). In addition, this study develops analyses of the mean square error (MSE) and the regularized zero-forcing (RZF) precoder under imperfect channel state information (CSI) and a realistic physical channel model. To this end, the MSE minimization and the spectral efficiency (SE) maximization are investigated. The results show that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated channels even though the MSE is considerably minimized. This implies that using a URA topology with relatively high channel correlations would not be beneficial to the SE unless an interference management scheme is exploited.
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Open AccessFeature PaperArticle
Investigation of a Novel CO2 Transcritical Organic Rankine Cycle Driven by Parabolic Trough Solar Collectors
Appl. Syst. Innov. 2021, 4(3), 53; https://doi.org/10.3390/asi4030053 - 09 Aug 2021
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The objective of the present study is the detailed investigation and optimization of a transcritical organic Rankine cycle operating with CO2. The novelty of the present system is that the CO2 is warmed up inside a solar parabolic trough collector
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The objective of the present study is the detailed investigation and optimization of a transcritical organic Rankine cycle operating with CO2. The novelty of the present system is that the CO2 is warmed up inside a solar parabolic trough collector and there is not a secondary circuit between the solar collector and the CO2. Therefore, the examined configuration presents increased performance due to the higher operating temperatures of the working fluid in the turbine inlet. The system is studied parametrically and it is optimized by investigating different pressure and temperature level in the turbine inlet. The simulation is performed with a validated mathematical model that has been developed in Engineering Equation Solver software. According to the results, the optimum turbine inlet temperature is ranged from 713 up to 847 K, while the higher pressure in the turbine inlet enhances electricity production. In the default scenario (turbine inlet at 800 K and turbine pressure at 200 bar), the system efficiency is found 24.27% with solar irradiation at 800 W/m2. A dynamic investigation of the system for Athens (Greece) climate proved that the yearly efficiency of the unit is 19.80%, the simple payback period of the investment is 7.88 years, and the yearly CO2 emissions avoidance is 48.7 tones.
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Open AccessReview
Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review
Appl. Syst. Innov. 2021, 4(3), 52; https://doi.org/10.3390/asi4030052 - 06 Aug 2021
Cited by 1
Abstract
The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In
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The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.
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(This article belongs to the Special Issue Feature Paper Collection in Applied System Innovation)
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Open AccessArticle
Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control
Appl. Syst. Innov. 2021, 4(3), 51; https://doi.org/10.3390/asi4030051 - 05 Aug 2021
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Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate
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Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.
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Open AccessArticle
Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
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Appl. Syst. Innov. 2021, 4(3), 50; https://doi.org/10.3390/asi4030050 - 04 Aug 2021
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The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge
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The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.
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Open AccessArticle
Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning
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Appl. Syst. Innov. 2021, 4(3), 49; https://doi.org/10.3390/asi4030049 - 04 Aug 2021
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Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients.
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Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.
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Open AccessArticle
Development of an Optical System for Non-Contact Type Measurement of Heart Rate and Heart Rate Variability
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Appl. Syst. Innov. 2021, 4(3), 48; https://doi.org/10.3390/asi4030048 - 28 Jul 2021
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Self-mixing optical coherent detection is a non-contact measurement technique which provides accurate information about the vibration frequency of any test subject. In this research, novel designs of optical homodyne and heterodyne detection techniques are explained. Homodyne and heterodyne setups are used for measuring
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Self-mixing optical coherent detection is a non-contact measurement technique which provides accurate information about the vibration frequency of any test subject. In this research, novel designs of optical homodyne and heterodyne detection techniques are explained. Homodyne and heterodyne setups are used for measuring the frequency of the modulated optical signal. This technique works on the principle of the optical interferometer, which provides a coherent detection of two self-mixing beams. In the optical homodyne technique, one of the two beams receives direct modulation from the vibration frequency of the test subject. In the optical heterodyne detection technique, one of the two optical beams is subjected to modulation by an acousto-optics modulator before becoming further modulated by the vibration frequency of the test subject. These two optical signals form an interference pattern that contains the information of the vibration frequency. The measurement of cardiovascular signals, such as heart rate and heart rate variability, are performed with both homodyne and heterodyne techniques. The optical coherent detection technique provides a high accuracy for the measurement of heart period and heart rate variability. The vibrocardiogram output obtained from both techniques are compared for different heart rate values. Results obtained from both optical homodyne and heterodyne detection techniques are compared and found to be within 1% of deviation value. The results obtained from both the optical techniques have a deviation of less than 1 beat per minute from their corresponding ECG values.
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Open AccessFeature PaperArticle
Coded Control of a Sectional Electroelastic Engine for Nanomechatronics Systems
Appl. Syst. Innov. 2021, 4(3), 47; https://doi.org/10.3390/asi4030047 - 28 Jul 2021
Abstract
This work determines the coded control of a sectional electroelastic engine at the elastic–inertial load for nanomechatronics systems. The expressions of the mechanical and adjustment characteristics of a sectional electroelastic engine are obtained using the equations of the electroelasticity and the mechanical load.
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This work determines the coded control of a sectional electroelastic engine at the elastic–inertial load for nanomechatronics systems. The expressions of the mechanical and adjustment characteristics of a sectional electroelastic engine are obtained using the equations of the electroelasticity and the mechanical load. A sectional electroelastic engine is applied for coded control of nanodisplacement as a digital-to-analog converter. The transfer function and the transient characteristics of a sectional electroelastic engine at elastic–inertial load are received for nanomechatronics systems.
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(This article belongs to the Special Issue Feature Paper Collection in Applied System Innovation)
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Open AccessArticle
Routing Performance Evaluation of a Multi-Domain Hybrid SDN for Its Implementation in Carrier Grade ISP Networks
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Appl. Syst. Innov. 2021, 4(3), 46; https://doi.org/10.3390/asi4030046 - 21 Jul 2021
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Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need of minimizing the capital and operational expenditure of running network infrastructure. The implementation of software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy
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Legacy IPv4 networks are strenuous to manage and operate. Network operators are in need of minimizing the capital and operational expenditure of running network infrastructure. The implementation of software-defined networking (SDN) addresses those issues by minimizing the expenditures in the long run. Legacy networks need to integrate with the SDN networks for smooth migration towards the fully functional SDN environment. In this paper, we compare the network performance of the legacy network with the SDN network for IP routing in order to determine the feasibility of the SDN deployment in the Internet Service provider (ISP) network. The simulation of the network is performed in the Mininet test-bed and the network traffic is generated using a distributed Internet traffic generator. An open network operating system is used as a controller for the SDN network, in which the SDN-IP application is used for IP routing. Round trip time, bandwidth, and packet transmission rate from both SDN and legacy networks are first collected and then the comparison is made. We found that SDN-IP performs better in terms of bandwidth and latency as compared to legacy routing. The experimental analysis of interoperability between SDN and legacy networks shows that SDN implementation in a production level carrier-grade ISP network is viable and progressive.
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Open AccessEditorial
Special Issue “Industry 5.0: The Prelude to the Sixth Industrial Revolution”
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Appl. Syst. Innov. 2021, 4(3), 45; https://doi.org/10.3390/asi4030045 - 11 Jul 2021
Abstract
While a significant number of companies around the world are still trying to adapt to Industry 4 [...]
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(This article belongs to the Special Issue Industry 5.0: The Prelude to the Sixth Industrial Revolution)
Open AccessArticle
Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
Appl. Syst. Innov. 2021, 4(3), 44; https://doi.org/10.3390/asi4030044 - 09 Jul 2021
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The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not
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The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.
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Open AccessArticle
Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting
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Appl. Syst. Innov. 2021, 4(3), 43; https://doi.org/10.3390/asi4030043 - 08 Jul 2021
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Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for
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Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.
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Open AccessOpinion
Intelligent Ventilation Systems in Mining Engineering: Is ZigBee WSN Technology the Best Choice?
by
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Appl. Syst. Innov. 2021, 4(3), 42; https://doi.org/10.3390/asi4030042 - 08 Jul 2021
Abstract
With the continuous development and progress of the mining industry, various technologies in mining engineering have gradually developed towards the intelligent stage, and the ventilation system is no exception. Since ancient times, mine ventilation has been a necessary part of mining engineering, and
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With the continuous development and progress of the mining industry, various technologies in mining engineering have gradually developed towards the intelligent stage, and the ventilation system is no exception. Since ancient times, mine ventilation has been a necessary part of mining engineering, and so the optimization of mine ventilation undoubtedly plays a great role in mining production. This two-part opinion paper briefly introduces the development of the intelligent ventilation in mining engineering and serves as a guide to the Tossing out a brick to get a jade gem, with implications for both the development and the future of the underground mine ventilation systems. Finally, in the second part of the paper, we explain why we think ZigBee WSN technology is the best choice in intelligent ventilation systems in underground mines at the present stage.
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Open AccessArticle
On the Use of Quality Models to Address Distinct Quality Views
Appl. Syst. Innov. 2021, 4(3), 41; https://doi.org/10.3390/asi4030041 - 02 Jul 2021
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Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of
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Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of 23 previously identified distinct software product quality models are analysed on how they handle the abstract notion of quality, and a taxonomy on the quality manifestations that the individual software product quality models are able to capture is established. Quality models that are able to solely describe the quality manifestation of the source code are attractive due to their full automation potential through static code analysers, but their assessment results ignore a huge part of software product quality, which is the one that most impresses the end user. The manifestations of software product quality that address the behaviour of the software while it operates, or the perception of the end user with regard to the software in use, require human involvement in the quality assessment. The taxonomy contributes to interpreting the quality assessment results of different quality models by showing the possible quality manifestations that can be captured by the identified models; moreover, the taxonomy also provides assistance while selecting a quality model for a given project. The quality manifestations used for the quality measurement always need to be presented, otherwise the quality assessment results cannot be interpreted in an appropriate manner.
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Open AccessPerspective
Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
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Appl. Syst. Innov. 2021, 4(3), 40; https://doi.org/10.3390/asi4030040 - 30 Jun 2021
Abstract
During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a
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During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.
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(This article belongs to the Special Issue Advances in Machine Learning and High-Performance Calculations for Innovative Technologies Development)
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Open AccessArticle
Value of the 3D Product Model Use in Assembly Processes: Process Planning, Design, and Shop Floor Execution
Appl. Syst. Innov. 2021, 4(2), 39; https://doi.org/10.3390/asi4020039 - 18 Jun 2021
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Organizations can enhance the value of their assembly planning, assembly design, and assembly shop floor execution through the use of the 3D product model. Once a tool targeted at product design, the 3D product model, enabled by current and emerging manufacturing process management
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Organizations can enhance the value of their assembly planning, assembly design, and assembly shop floor execution through the use of the 3D product model. Once a tool targeted at product design, the 3D product model, enabled by current and emerging manufacturing process management technologies, can create additional value for organizations when used in assembly processes. The research survey conducted and described in this paper demonstrates the value organizations have seen in using the 3D product model in the assembly process. The paper also explores the current state of those organizations who have not yet implemented the use of the 3D product model in their assembly processes and the value that they foresee for possible future implementation. The essential findings of this research are the five qualitative areas in which value is derived from using the 3D product model in complex assembly processes and how those value drivers apply across various industries and organization sizes. These results provide a framework for future research to develop quantitative models of the value of the 3D product model use in assembly processes.
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Smart Grids and Contemporary Electricity Markets
Guest Editor: Emmanuel KarapidakisDeadline: 30 September 2021
Special Issue in
ASI
Systems and Industries in Response to COVID-19 Crisis
Guest Editors: Zeeshan Asim, Vinitha Guptan, Shahryar SorooshianDeadline: 31 October 2021
Special Issue in
ASI
Intelligent Industrial Application of Communication Systems
Guest Editor: Claudio ZuninoDeadline: 30 November 2021
Special Issue in
ASI
Non-linear Devices, Systems, Networks and Their Applications
Guest Editor: Ludovico MinatiDeadline: 31 December 2021
Topical Collections
Topical Collection in
ASI
Feature Paper Collection on Civil Engineering and Architecture
Collection Editor: Luís Bragança





