Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 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.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Improved Convolutional Neural Network–Time-Delay Neural Network Structure with Repeated Feature Fusions for Speaker Verification
Appl. Sci. 2024, 14(8), 3471; https://doi.org/10.3390/app14083471 (registering DOI) - 19 Apr 2024
Abstract
The development of deep learning greatly promotes the progress of speaker verification (SV). Studies show that both convolutional neural networks (CNNs) and dilated time-delay neural networks (TDNNs) achieve advanced performance in text-independent SV, due to their ability to sufficiently extract the local feature
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The development of deep learning greatly promotes the progress of speaker verification (SV). Studies show that both convolutional neural networks (CNNs) and dilated time-delay neural networks (TDNNs) achieve advanced performance in text-independent SV, due to their ability to sufficiently extract the local feature and the temporal contextual information, respectively. Also, the combination of the above two has achieved better results. However, we found a serious gridding effect when we apply the 1D-Res2Net-based dilated TDNN proposed in ECAPA-TDNN for SV, which indicates discontinuity and local information losses of frame-level features. To achieve high-resolution process for speaker embedding, we improve the CNN–TDNN structure with proposed repeated multi-scale feature fusions. Through the proposed structure, we can effectively improve the channel utilization of TDNN and achieve higher performance under the same TDNN channel. And, unlike previous studies that have all converted CNN features to TDNN features directly, we also studied the latent space transformation between CNN and TDNN to achieve efficient conversion. Our best method obtains 0.72 EER and 0.0672 MinDCF on VoxCeleb-O test set, and the proposed method performs better in cross-domain SV without additional parameters and computational complexity.
Full article
(This article belongs to the Special Issue Audio, Speech and Language Processing)
Open AccessArticle
Determination of Self-Neutralization Phenomena of Ion Beams with Langmuir Probe Measurements and PIC-DSMC Simulations
by
Ruslan Kozakov, Maximilian Maigler, Jochen Schein and Neil Wallace
Appl. Sci. 2024, 14(8), 3470; https://doi.org/10.3390/app14083470 (registering DOI) - 19 Apr 2024
Abstract
Small -class gridded ion thrusters are usually tested in a vacuum chamber without the use of a neutralizer, relying on self-neutralization of the ion beam due to interaction with facility walls. Langmuir probe measurements performed immediately downstream of such a thruster
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Small -class gridded ion thrusters are usually tested in a vacuum chamber without the use of a neutralizer, relying on self-neutralization of the ion beam due to interaction with facility walls. Langmuir probe measurements performed immediately downstream of such a thruster reveal values of the plasma potential of several hundreds of volts. If this locally very high potential indeed exists, it would have significant impact on the erosion rate of RIT grids and thus reduce the lifetime of thrusters compared to the generally accepted plasma potential of a few tens of volts. Further measurements performed with a movable Langmuir and emissive probes indicate that the probe mount violates the ability of the ion beam to self-neutralize. This is concluded due to dependence of the measured potential value on the degree of neutralization introduced in the experiment. Particle-in-cell and direct-simulation Monte Carlo simulations of the ion beam corresponding to experimental conditions (ion energy and ion beam current ) are carried out to determine the phenomena responsible for the self-neutralization; mainly, reactions with neutral species such as ionization by electron or ion impact and secondary electron emission (SEE) from the facility walls are compared. Reasonable agreement with measurements is achieved, and SEE is determined to be the primary source of electrons, indicating that facility and measurement disturbance effects majorly influence testing of (non-neutralized) ion beams. Further, limitations of the applicability of probe diagnostics on non-neutralized ion beams are described.
Full article
(This article belongs to the Section Aerospace Science and Engineering)
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Open AccessArticle
Assessment of the Application Possibilities of Dried Walnut Leaves (Juglans regia L.) in the Production of Wheat Bread
by
Karolina Pycia, Agata Maria Pawłowska and Joanna Kaszuba
Appl. Sci. 2024, 14(8), 3468; https://doi.org/10.3390/app14083468 (registering DOI) - 19 Apr 2024
Abstract
The main aim of this work was to assess the possibility of using dried walnut leaves (Juglans regia L.) in the production of wheat bread. In the developed recipe, wheat flour was partially replaced with dried and powdered walnut leaves (WLs) in
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The main aim of this work was to assess the possibility of using dried walnut leaves (Juglans regia L.) in the production of wheat bread. In the developed recipe, wheat flour was partially replaced with dried and powdered walnut leaves (WLs) in amounts of 0.5%, 1.0%, 1.5%, 2.0% and 2.5%. Serving as a control sample was wheat bread without WLs. The bread was made using a single-phase method using yeast. As part of the research methodology, laboratory baking parameters (dough yield, total oven loss, bread yield), loaf volume and loaf specific volume were determined. Additionally, the colors of the dough, crust and bread crumb were determined in the CIE L*a*b* space. The crumb texture profile was assessed using the TPA test. Additionally, the antioxidant power and the total phenolic content of the dough and bread were tested. The consumer acceptability of the sensory features of the bread was also assessed. The test results were statistically analyzed using a one-way ANOVA. It was found that enriching the bread recipe with WLs had a significant impact on its quality. The control bread had the highest volume, and replacing wheat flour with dried leaves resulted in a significant reduction in volume, which was greater at higher WLs contents. Similarly, as the proportion of WLs in the recipe increased, a significant decrease in the brightness of both the dough and the crumb of the obtained bread was observed. A texture analysis showed that bread with a WLs content of 2.5% was the most different from the control bread and other tested breads. However, it was found that enriching bread with the unconventional addition of walnut leaves had a very good effect on its health benefits. The antioxidant activity and the content of total phenolics in the bread grew with the increase in the share of WLs in the recipe.
Full article
(This article belongs to the Special Issue Advanced Food Processing Technologies and Food Quality)
Open AccessArticle
A Statistical Mesoscale Approach to Model the Size Effect on the Tensile Strength of Notched Woven Composites
by
Andrea Ferrarese, Carlo Boursier Niutta, Alberto Ciampaglia and Davide Salvatore Paolino
Appl. Sci. 2024, 14(8), 3467; https://doi.org/10.3390/app14083467 (registering DOI) - 19 Apr 2024
Abstract
The scaling of the strength of composite parts with part size is referred to as the size effect. In the presence of notches, stress concentration affects a portion of material that increases with the notch size. Furthermore, in woven composites, the notch and
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The scaling of the strength of composite parts with part size is referred to as the size effect. In the presence of notches, stress concentration affects a portion of material that increases with the notch size. Furthermore, in woven composites, the notch and tow size can be comparable, thus demanding a mesoscale approach to properly capture the stress intensification. In this paper, a probabilistic mesoscale method to model the size effect in notched woven composites is presented. First, the stress distribution is estimated with a finite element model, calibrated on experimental Digital Image Correlation data. The FE model simulates the mesoscale heterogeneity of the woven reinforced material and replicates the local stress intensification at the tow level. Then, a three-parameter Weibull-based statistical model is introduced to model the probability of failure from the calculated stress distribution and the volume of the part. An equivalent stress is used to capture the relevant fiber and matrix failure modes and the maximum value within the specimen volume is the random variable of the model. The method is applied to open-hole tension tests of a woven twill carbon fiber–epoxy composite. Two specimen widths and three width-to-diameter ratios, from 3 to 12, are considered. Specimen width produced an observable size effect, whereas the variation of hole size in the range considered did not. The statistical model is found to accurately describe the experimental observations, efficiently replicating an inverse size effect, regardless of hole size, while wider specimens lead to a lower probability of failure.
Full article
(This article belongs to the Special Issue Mechanical Properties and Fatigue Behavior of Composite Materials)
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Open AccessArticle
Offline Identification of a Laboratory Incubator
by
Süleyman Mantar and Ersen Yılmaz
Appl. Sci. 2024, 14(8), 3466; https://doi.org/10.3390/app14083466 (registering DOI) - 19 Apr 2024
Abstract
Laboratory incubators are used to maintain and cultivate microbial and cell cultures. In order to ensure suitable growing conditions and to avoid cell injuries and fast rise and settling times, minimum overshoot and undershoot performance indexes should be considered in the controller design
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Laboratory incubators are used to maintain and cultivate microbial and cell cultures. In order to ensure suitable growing conditions and to avoid cell injuries and fast rise and settling times, minimum overshoot and undershoot performance indexes should be considered in the controller design for incubators. Therefore, it is important to build proper models to evaluate the performance of the controllers before implementation. In this study, we propose an approach to build a model for a laboratory incubator. In this approach, the incubator is considered a linear time-invariant single-input, single-output system. Four different model structures, namely auto-regressive exogenous, auto-regressive moving average exogenous, output error and Box–Jenkins, are applied for modeling the system. The parameters of the model structures are estimated by using prediction error methods. The performances of the model structures are evaluated in terms of mean squared error, mean absolute error and goodness of fit. Additionally, residue analysis including auto-correlation and cross-correlation plots is provided. Experiments are carried out in two scenarios. In the first scenario, the identification dataset is collected from the unit-step response, while in the second scenario, it is collected from the pseudorandom binary sequence response. The experimental study shows that the Box–Jenkins model achieves an over 90% fit percentage for the first scenario and an over 95% fit percentage for the second scenario. Based on the experimental results, it is concluded that the Box–Jenkins model can be used as a successful model for laboratory incubators.
Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)
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Open AccessArticle
Cylindrical Steel Tanks Subjected to Long-Duration and High-Pressure Triangular Blast Load: Current Practice and a Numerical Case Study
by
Julia Rosin, Alessandro Stocchi, Norman Bruckhaus, Johanna Heyner, Philipp Weidner and Till Waas
Appl. Sci. 2024, 14(8), 3465; https://doi.org/10.3390/app14083465 (registering DOI) - 19 Apr 2024
Abstract
This paper presents an investigation into the design of ammonia tanks for long-duration and high-pressure blast loads. The focus is on cylindrical steel tanks that apply as outer pressure-tight containers for double-walled tanks storing refrigerated liquefied gases. Based on limited empirical data, it
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This paper presents an investigation into the design of ammonia tanks for long-duration and high-pressure blast loads. The focus is on cylindrical steel tanks that apply as outer pressure-tight containers for double-walled tanks storing refrigerated liquefied gases. Based on limited empirical data, it is known in the tank industry that these tanks can withstand an explosion pressure up to a peak overpressure of approximately 10 kPa and 100 ms positive load duration. However, there is a growing need to design tanks for higher peak overpressures in order to establish a higher safety standard and accommodate unforeseen future requirements. This paper explores the concept of adapting established steel tank designs to handle high-pressure and long-duration overpressure due to blast events. Numerical analysis is conducted on a representative steel tank geometry subjected to a triangular blast load of 30 kPa with a 300 ms positive load duration. Various load application and calculation options are analyzed numerically. Considering the challenging nature of analyzing tank structures under blast load, the paper addresses controversial aspects discussed in the literature and presents a suitable analysis concept for a deflagration blast scenario for cylindrical tanks. The results provide insights into the expected structural behavior of the tank under high-pressure and long-duration overpressure. The main finding is that the calculation method developed in this study demonstrates the feasibility of utilizing steel tanks in scenarios involving long-duration and high-pressure blast loads. Furthermore, the paper provides recommendations to guide future studies in this area. The findings have implications for the design and construction of tanks in critical infrastructure and offer valuable insights for engineers and researchers in this field, improving safety standards and ensuring adaptability to future utilization concepts.
Full article
(This article belongs to the Special Issue Recent Advances in the Effect of Blast Loads on Structures)
Open AccessArticle
Comparing Several P300-Based Visuo-Auditory Brain-Computer Interfaces for a Completely Locked-in ALS Patient: A Longitudinal Case Study
by
Rute Bettencourt, Miguel Castelo-Branco, Edna Gonçalves, Urbano J. Nunes and Gabriel Pires
Appl. Sci. 2024, 14(8), 3464; https://doi.org/10.3390/app14083464 (registering DOI) - 19 Apr 2024
Abstract
In a completely locked-in state (CLIS), often resulting from traumatic brain injury or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), patients lose voluntary muscle control, including eye movement, making communication impossible. Brain-computer interfaces (BCIs) offer hope for restoring communication, but achieving reliable communication
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In a completely locked-in state (CLIS), often resulting from traumatic brain injury or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), patients lose voluntary muscle control, including eye movement, making communication impossible. Brain-computer interfaces (BCIs) offer hope for restoring communication, but achieving reliable communication with these patients remains a challenge. This study details the design, testing, and comparison of nine visuo-auditory P300-based BCIs (combining different visual and auditory stimuli and different visual layouts) with a CLIS patient over ten months. The aim was to evaluate the impact of these stimuli in achieving effective communication. While some interfaces showed promising progress, achieving up to 90% online accuracy in one session, replicating this success in subsequent sessions proved challenging, with the average online accuracy across all sessions being 56.4 ± 15.2%. The intertrial variability in EEG signals and the low discrimination between target and non-target events were the main challenge. Moreover, the lack of communication with the patient made BCI design a challenging blind trial-and-error process. Despite the inconsistency of the results, it was possible to infer that the combination of visual and auditory stimuli had a positive impact, and that there was an improvement over time.
Full article
(This article belongs to the Special Issue Brain-Computer Interfaces: Novel Technologies and Applications)
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Open AccessArticle
Identifying Correlated Functional Brain Network Patterns Associated with Touch Discrimination in Survivors of Stroke Using Automated Machine Learning
by
Alistair Walsh, Peter Goodin and Leeanne M. Carey
Appl. Sci. 2024, 14(8), 3463; https://doi.org/10.3390/app14083463 (registering DOI) - 19 Apr 2024
Abstract
Stroke recovery is multifaceted and complex. Machine learning approaches have potential to identify patterns of brain activity associated with clinical outcomes, providing new insights into recovery. We aim to use machine learning to characterise the contribution of and potential interaction between resting state
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Stroke recovery is multifaceted and complex. Machine learning approaches have potential to identify patterns of brain activity associated with clinical outcomes, providing new insights into recovery. We aim to use machine learning to characterise the contribution of and potential interaction between resting state functional connectivity networks in predicting touch discrimination outcomes in a well-phenotyped, but small, stroke cohort. We interrogated and compared a suite of automated machine learning approaches to identify patterns of brain activity associated with clinical outcomes. Using feature reduction, the identification of combined ‘golden features’, and five-fold cross-validation, two golden features patterns emerged. These golden features identified patterns of resting state connectivity involving interactive relationships: 1. The difference between right insula and right superior temporal lobe correlation and left cerebellum and vermis correlation; 2. The ratio between right inferior temporal lobe and left cerebellum correlation and left frontal inferior operculum and left supplementary motor area correlation. Our findings demonstrate evidence of the potential for automated machine learning to provide new insights into brain network patterns and their interactions associated with the prediction of quantitative touch discrimination outcomes, through the automated identification of robust associations and golden feature brain patterns, even in a small cohort of stroke survivors.
Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Neuroscience)
Open AccessArticle
Impact of Selected Yeast Strains on Quality Parameters of Obtained Sauerkraut
by
Paweł Satora and Szymon Strnad
Appl. Sci. 2024, 14(8), 3462; https://doi.org/10.3390/app14083462 (registering DOI) - 19 Apr 2024
Abstract
The aim of this research was to determine the influence of yeast strains (previously isolated from the fermentation process) on selected quality parameters of sauerkraut. For this purpose, shredded and salted (2.5% w/w) cabbage of the Galaxy variety was fermented
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The aim of this research was to determine the influence of yeast strains (previously isolated from the fermentation process) on selected quality parameters of sauerkraut. For this purpose, shredded and salted (2.5% w/w) cabbage of the Galaxy variety was fermented in the absence of oxygen with the addition of 2 × 106 cells of a selected yeast culture. The control sample was spontaneously fermented sauerkraut without yeast addition. The obtained sauerkraut was analysed in terms of the content of selected organic acids, sugars and polyols (HPLC), selected volatile compounds (HS-SPME-GC-TOFMS), colour (CieLAB) and aroma (QDA). Yeast P. fermentans, Rh. mucilaginosa and W. anomalus reduced crucial sauerkraut components such as lactic acid, glycerol, and certain volatile compounds, leading to decreased aroma intensity and acceptability. Additionally, an increase in glucosinolate decomposition products was observed. Conversely, D. hansenii positively influenced sauerkraut quality by enhancing lactic acid content and exhibiting similar volatile characteristics to those of the control. Two of the three samples fermented with D. hansenii received high sensory analysis scores akin to those of the control. Sauerkraut fermented with Cl. lusitaniae yeast contained elevated levels of volatile compounds—alcohols, esters and lactones—resulting in an intense floral aroma, albeit receiving lower overall ratings due to deviation from the typical profile.
Full article
(This article belongs to the Special Issue Role of Microbes in Agriculture and Food, 2nd Edition)
Open AccessArticle
Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
by
Marian Kampik, Jerzy Roj and Łukasz Dróżdż
Appl. Sci. 2024, 14(8), 3461; https://doi.org/10.3390/app14083461 (registering DOI) - 19 Apr 2024
Abstract
This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities
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This paper presents an error model of a measurement chain containing a link that executes a discrete wavelet transform algorithm, which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both the deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.
Full article
(This article belongs to the Topic Uncertainty Quantification in Design, Manufacturing and Maintenance of Complex Systems)
Open AccessArticle
Research and Application of an Improved Sparrow Search Algorithm
by
Liwei Hu and Denghui Wang
Appl. Sci. 2024, 14(8), 3460; https://doi.org/10.3390/app14083460 (registering DOI) - 19 Apr 2024
Abstract
Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining.
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Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm’s capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes.
Full article
(This article belongs to the Special Issue Big Data: Analysis, Mining and Applications)
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Open AccessArticle
Study of the Static Characteristics of Gas-Lubricated Thrust Bearings Using Analytical and Finite Element Methods
by
Ke Zhang, Xiaojiao Zhang and Ruiyu Zhang
Appl. Sci. 2024, 14(8), 3459; https://doi.org/10.3390/app14083459 (registering DOI) - 19 Apr 2024
Abstract
A study was conducted to develop a porous aerostatic rectangular thrust bearing model, with the aim of assessing how different operational conditions and geometric factors influence its static capabilities. Initially, the Reynolds equation was analytically solved. Subsequently, simulations were performed on the rectangular
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A study was conducted to develop a porous aerostatic rectangular thrust bearing model, with the aim of assessing how different operational conditions and geometric factors influence its static capabilities. Initially, the Reynolds equation was analytically solved. Subsequently, simulations were performed on the rectangular air bearing model. Analyzing the impact of throttle hole configurations, air film thickness, orifice size, and supply pressure revealed their significant effect on the bearing’s load capacity, air consumption, peak airflow speed in the air film gap, and rigidity. Experimental validations were further conducted on manufactured bearings, corroborating the theoretical findings. It was observed that extending the length of the rectangular throttle hole array progressively increases gas consumption and diminishes stability, while the load capacity and stiffness initially surge then taper off. A thinner air film enhances load capacity and reduces gas flow, contributing to increased stability. Conversely, enlarging the orifice diameter boosts both load capacity and stability but escalates mass flow and diminishes stiffness. Elevating gas supply pressure enhances load capacity, flow rate, and stiffness, albeit at the cost of reduced stability. A comparative analysis among experimental data, finite element analysis, and analytical solutions showed strong congruence, affirming the precision of the latter two methods for predicting the bearing’s performance. This investigation aids with refining bearing design for precision devices and offers insights to enhance bearing efficiency and lifespan and to reduce friction and wear. Given its lower computational demands, the analytical approach provides a rapid means to assess static characteristics, underscoring its utility alongside finite element techniques for optimizing aerostatic bearing parameters.
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Open AccessArticle
Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers
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Sairoel Amertet, Girma Gebresenbet and Hassan Mohammed Alwan
Appl. Sci. 2024, 14(8), 3458; https://doi.org/10.3390/app14083458 (registering DOI) - 19 Apr 2024
Abstract
Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different
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Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.
Full article
(This article belongs to the Section Agricultural Science and Technology)
Open AccessArticle
Study on the Macro-Fine Mechanical Behavior of Ore Flow Based on the Discrete Element Method
by
Zhiguo Xia, Zhe Deng, Zengxiang Lu and Chenglong Ma
Appl. Sci. 2024, 14(8), 3457; https://doi.org/10.3390/app14083457 (registering DOI) - 19 Apr 2024
Abstract
The mechanical behavior associated with the flow of ore-rock bulk materials is an important factor leading to the instability and failure of the shaft wall of the ore storage section in ore passes. It is of great significance for accurately understanding the stability
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The mechanical behavior associated with the flow of ore-rock bulk materials is an important factor leading to the instability and failure of the shaft wall of the ore storage section in ore passes. It is of great significance for accurately understanding the stability failure characteristics of the shaft wall in the ore storage section in the ore-drawing process to understand the flow characteristics and internal mechanical transfer mechanism of ore-rock bulk. The flow characteristics, contact compactness, stress distribution characteristics, and contact force probability distribution of the ore-rock bulk are analyzed by the discrete element method, which realizes the quantitative characterization of the damage degree of ore-rock flow and reveals the damage mechanism of the shaft wall in the storage section of the ore pass. The results show that (1) in the process of ore-rock particle flow in the ore pass storage section, the macroscopic flow pattern of ore-rock particles changes from a “—” shape to a “V” shape, and the friction between ore-rock particles, particles, and the ore-pass wall is an important reason for the change of the macroscopic flow pattern; (2) the probability distribution of contact force strength between the particles decreases exponentially in the whole ore-drawing process, in which the strong force chains play a major role in the stability of the bulk system; and (3) the overpressure frequency and overpressure coefficient could be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic lateral pressure fluctuates periodically in exponential form and decreases, and the dynamic load formed by the ore-rock flow mainly acts on the lower part of the ore storage section.
Full article
(This article belongs to the Special Issue Advanced Methodology and Analysis in Coal Mine Gas Control)
Open AccessArticle
Difference in Stiffness between Biceps Brachii Muscle Bellies Using Shear Wave Elastography
by
Jacqueline Roots, Gabriel S. Trajano, Adam Bretherton, Christopher Drovandi and Davide Fontanarosa
Appl. Sci. 2024, 14(8), 3456; https://doi.org/10.3390/app14083456 (registering DOI) - 19 Apr 2024
Abstract
The Shear Wave Elastography of muscles can provide real-time information on the stiffness of muscles; however, the difference in stiffness between biceps brachii muscle bellies requires more research. Understanding the variables that affect muscle stiffness will assist in the development of Shear Wave
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The Shear Wave Elastography of muscles can provide real-time information on the stiffness of muscles; however, the difference in stiffness between biceps brachii muscle bellies requires more research. Understanding the variables that affect muscle stiffness will assist in the development of Shear Wave Elastography as a diagnostic tool for muscle stiffness pathologies. This study’s aim is to determine the Shear Wave Velocity of the short and long head of biceps brachii and the change in stiffness with elbow flexion to create a reliable protocol for pathological muscle assessment. The muscle belly of the short and long heads of bilateral biceps brachii of 38 healthy participants were scanned supine with the arm at full extension and at 30° and 60° elbow flexion. A log transform of the SWV was used as the response variable in the regression analysis, and the intraclass correlation coefficient was determined for reliability. The Shear Wave Velocity of the short head was lower than the long head on average. By fitting Bayesian mixed effect regression models to the data, the estimated posterior predictive mean velocities for the short head at full extension, 30°, and 60° were 3.14 m/s, 2.65 m/s, and 2.62 m/s, respectively; and 3.91 m/s, 3.02 m/s, and 3.15 m/s, respectively, for the long head of the biceps brachii. The intraclass correlation coefficients (0.64–0.92) were good to excellent. Shear Wave Elastography can detect the consistent difference in the stiffness of the two muscle bellies of the biceps brachii at multiple elbow angles. The assessment of muscle stiffness with Shear Wave Elastography should consider the morphology of the muscles.
Full article
(This article belongs to the Special Issue Current Updates on Ultrasound for Biomedical Applications)
Open AccessArticle
Study on the Oil Spill Transport Behavior and Multifactorial Effects of the Lancang River Crossing Pipeline
by
Jingyang Lu, Liqiong Chen and Duo Xu
Appl. Sci. 2024, 14(8), 3455; https://doi.org/10.3390/app14083455 - 19 Apr 2024
Abstract
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As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers.
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As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model’s accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R2) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents.
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Open AccessArticle
Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers
by
Shailesh Tripathi, Nadine Bachmann, Manuel Brunner and Herbert Jodlbauer
Appl. Sci. 2024, 14(8), 3454; https://doi.org/10.3390/app14083454 - 19 Apr 2024
Abstract
This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized
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This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.
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(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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Open AccessArticle
High-Spatial-Resolution Helium Detection and Its Implications for Helium Accumulation Mechanisms
by
Chao Lu, Bang Wang, Di Zhu, Quanyou Liu, Xuhang Zhang and Huaiyu He
Appl. Sci. 2024, 14(8), 3453; https://doi.org/10.3390/app14083453 - 19 Apr 2024
Abstract
Helium is a scarce strategic resource. Currently, all economically valuable helium resources are found in natural gas reservoirs. Owing to helium’s different formation and migration processes compared to natural gas’s, the traditional method of collecting wellhead gas to detect helium concentration may miss
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Helium is a scarce strategic resource. Currently, all economically valuable helium resources are found in natural gas reservoirs. Owing to helium’s different formation and migration processes compared to natural gas’s, the traditional method of collecting wellhead gas to detect helium concentration may miss helium-rich layers in the vertical direction, which will not only cause the waste of helium resources, but also restrict the study of helium migration and accumulation mechanisms. To solve this problem, we designed a helium detector based on a quadrupole mass spectrometer. Through the combination of different inlet valves, we avoided gas mixing between different vertical layers during the inlet process and realized high-spatial-resolution helium concentration detection. We applied the helium detector to the Dongsheng gas field in the northern Ordos Basin, and the instrumental detection results were consistent with the laboratory analysis results of the wellhead gas, which demonstrated the stability of the helium detector in the field environment and the reliability of the data. Meanwhile, the results showed that the distribution of helium in the plane is highly heterogeneous, and the natural gas dessert layers and the helium dessert layers do not coincide in the vertical direction. In addition, we found a good correlation between helium and hydrogen concentrations. Combining our results with previous data, we propose a hydrogen–helium migration and accumulation model, which enriches the understanding of helium accumulation mechanisms and provides a basis for future helium resource exploration.
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(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources)
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Open AccessCommunication
Mechanical Faults Analysis in Switched Reluctance Motor
by
Jakub Lorencki and Stanisław Radkowski
Appl. Sci. 2024, 14(8), 3452; https://doi.org/10.3390/app14083452 - 19 Apr 2024
Abstract
The switched reluctance motor (SRM) is an electric motor that can function effectively in challenging operating conditions thanks to its sturdy construction and resilience to external factors. Despite somewhat weaker parameters in terms of energy and power density compared to other types of
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The switched reluctance motor (SRM) is an electric motor that can function effectively in challenging operating conditions thanks to its sturdy construction and resilience to external factors. Despite somewhat weaker parameters in terms of energy and power density compared to other types of electric motors, the SRM is recommended for applications such as the military, mining, industry, and other locations where the reliability of vehicle drive is essential. Therefore, monitoring the motor’s operating state and identifying the fault’s condition while it is still in the beginning phase is crucial. The paper presents SRM diagnostic methods and the authors’ research on the test stand. The examined faults were dynamic eccentricity and imbalance. Experiments were performed for various rotational speeds and loads. The analysis of the results consisted of the interpretation of the current and acceleration spectra acquired from proper sensors. The spectra bands are compared in terms of their amplitudes and frequency values. These results show the nonlinear characteristics of the motor’s operation, and interpretation of these results allows for estimating the impact of a fault parameter on a motor’s performance.
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(This article belongs to the Section Transportation and Future Mobility)
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Open AccessArticle
Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
by
Ganglong Duan, Shaoyang Zhang, Yanying Shang and Weiwei Kong
Appl. Sci. 2024, 14(8), 3451; https://doi.org/10.3390/app14083451 - 19 Apr 2024
Abstract
Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients,
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Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen’s kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy.
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(This article belongs to the Special Issue Deep Learning for Image Recognition and Processing)
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