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
Development and Implementation of Algorithms for an Intelligent IGBT Gate Driver Using a Low-Cost Microcontroller
Appl. Sci. 2024, 14(10), 4247; https://doi.org/10.3390/app14104247 (registering DOI) - 16 May 2024
Abstract
High-power IGBTs are used in power electronic converters in a variety of applications: traction drives, renewable power converters, mining equipment, oil and water pumping, and so on. To control a transistor, a special gate driver board is required. This board converts the logical
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High-power IGBTs are used in power electronic converters in a variety of applications: traction drives, renewable power converters, mining equipment, oil and water pumping, and so on. To control a transistor, a special gate driver board is required. This board converts the logical control signal into the appropriate voltage values necessary to turn the resistor on and off. Gate drivers can perform the protection functions of IGBTs using hardware and algorithmic approaches. Application-specific integrated circuits are often used in driver solutions to implement control and protection. The development of an application-specific integrated circuit is a time-consuming and expensive procedure, which increases the cost of the driver. This paper describes the control and protection algorithms implemented in an intelligent IGBT driver based on a low-cost microcontroller. The use of the microcontroller makes the gate driver design more flexible and allows for the accurate tuning of the protection thresholds. The gate driver protects the IGBT from short-circuiting, overcurrent, and overvoltage, monitors the voltage supply, and controls the switch on and switch off processes in the transistor. The performance of the protection algorithms was tested experimentally using a specialized test bench.
Full article
(This article belongs to the Special Issue Power Electronics and Energy Storages for Automotive Industry and Renewable Energy Networks)
Open AccessArticle
Strategic Roadmap for Adopting Data-Driven Proactive Measures in Solar Logistics
by
Madhura Bhandigani, Akram Pattan and Silvia Carpitella
Appl. Sci. 2024, 14(10), 4246; https://doi.org/10.3390/app14104246 (registering DOI) - 16 May 2024
Abstract
This study presents a comprehensive overview of the solar industry’s transition towards resilient energy solutions, emphasizing the critical role of data-driven practices in driving this transition through responsible resource management. As continuous technological refinement is essential to optimize solar energy’s potential, the smart
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This study presents a comprehensive overview of the solar industry’s transition towards resilient energy solutions, emphasizing the critical role of data-driven practices in driving this transition through responsible resource management. As continuous technological refinement is essential to optimize solar energy’s potential, the smart use of available data plays a significant part in enhancing the accessibility of solar panels. Building upon prior research investigating the influence of Big Data on solar logistics, this paper proposes a hybrid Multi-Criteria Decision-Making (MCDM) methodology based on expert experience, providing practical support in the implementation of data-driven proactive measures within the solar industry. Specifically, this study focuses on measures aimed at effectively implementing two main logistic strategies, which are Route Optimization (RO) and Warehouse Management (WM). A rigorous analysis of criteria and measures considered to be relevant in the literature is first conducted. Criteria will be screened and weighted to eventually act as drivers toward measure assessment and prioritization. A final sensitivity analysis culminates in the formalization of findings and in the formulation of a pragmatic roadmap tailored for solar industry practitioners, designed to increase operational efficiency while integrating key sustainability principles across supply chain endeavors.
Full article
(This article belongs to the Special Issue Digital, Resilient and Sustainable Supply Chains: Research Trends and Future Challenges)
Open AccessArticle
Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
by
Renhong Wu, Yuqing Fan, Liuya Zhang, Debao Yuan and Guitang Gao
Appl. Sci. 2024, 14(10), 4245; https://doi.org/10.3390/app14104245 - 16 May 2024
Abstract
Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and
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Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R2 greater than 0.6, with validation accuracy R2 values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm−2, indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making.
Full article
(This article belongs to the Section Agricultural Science and Technology)
Open AccessReview
Virtual Reality Applications for Balance Rehabilitation and Efficacy in Addressing Other Symptoms in Multiple Sclerosis—A Review
by
Elena Bianca Basalic, Nadinne Roman, Vlad Ionut Tuchel and Roxana Steliana Miclăuș
Appl. Sci. 2024, 14(10), 4244; https://doi.org/10.3390/app14104244 - 16 May 2024
Abstract
(1) Background: Since multiple sclerosis (MS) is a neurological pathology known for its disabling effects across many domains, the introduction of virtual reality (VR) usage has been attempted, as it represents a new method of approach to rehabilitation and treatment of chronic neurological
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(1) Background: Since multiple sclerosis (MS) is a neurological pathology known for its disabling effects across many domains, the introduction of virtual reality (VR) usage has been attempted, as it represents a new method of approach to rehabilitation and treatment of chronic neurological pathologies. Encouraging research has explored the use of video game consoles and VR-assisted Robot-Assisted Gait Training (RAGT) to address balance disturbances in this population. (2) Methods: The search involved two databases, Web of Science and PubMed, utilizing a selection of terms including “Virtual reality”, “Multiple sclerosis”, “Balance”, and “Rehabilitation”. Two reviewers initiated and performed the search for articles, subsequently selecting and extracting data from the studies. The PEDro scale was the tool for evaluating the quality of the articles that we introduced in our research. (3) Results: After rigorous scanning, nine articles remained eligible for our study. VR interventions, particularly compared to standard balance training, consistently improved balance in multiple sclerosis. Robotic-assisted technology with 2D VR yielded superior results in balance rehabilitation. VR interventions had varied effects on walking speed. They have shown promise in decreasing the risk of falls and improving patients’ daily lives while reducing fatigue in multiple sclerosis. (4) Conclusions: VR offers comparable or superior benefits to classical exercise and no intervention for balance but does not significantly improve functional mobility. However, it shows the potential to improve quality of life and fatigue in MS patients. Investigation of VR alongside RAGT is important to be performed with larger sample sizes and comprehensive results are needed to fully understand its efficacy in MS rehabilitation.
Full article
(This article belongs to the Special Issue The Use of Virtual Reality (VR) in Medical Rehabilitation: Assessment Tools, Application Methods, VR Technology and Clinical Applications)
Open AccessArticle
A Novel Security Risk Analysis Using the AHP Method in Smart Railway Systems
by
İsa Avcı and Murat Koca
Appl. Sci. 2024, 14(10), 4243; https://doi.org/10.3390/app14104243 - 16 May 2024
Abstract
Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will
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Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5–15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%.
Full article
(This article belongs to the Topic Cyber Security and Critical Infrastructures, 2nd Edition)
Open AccessArticle
Study of Biocomposite Films Based on Cassava Starch and Microcrystalline Cellulose Derived from Cassava Pulp for Potential Medical Packaging Applications
by
Rachasit Jeencham, Nantawat Chiaoketwit, Piya-on Numpaisal and Yupaporn Ruksakulpiwat
Appl. Sci. 2024, 14(10), 4242; https://doi.org/10.3390/app14104242 - 16 May 2024
Abstract
This study aimed to develop biocomposite films based on cassava starch and microcrystalline cellulose (MCC) derived from cassava pulp for potential medical packaging applications. MCC was extracted from cassava pulp, and its structure and chemical composition, crystallinity, and thermal properties were characterized. The
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This study aimed to develop biocomposite films based on cassava starch and microcrystalline cellulose (MCC) derived from cassava pulp for potential medical packaging applications. MCC was extracted from cassava pulp, and its structure and chemical composition, crystallinity, and thermal properties were characterized. The MCC showed a yield of 14.92% and crystallinity of 46.91%. Different MCC contents (1%, 3%, and 5% w/w of starch) were incorporated into cassava starch films. The effects of MCC contents on film properties, including morphology, thickness, mechanical strength, chemical interactions, moisture content, surface wettability, and water activity index, were studied. The effects of UV-C sterilization on the disinfection of starch/MCC on film properties were determined. Results showed that all starch/MCC films exhibited good transparency and thickness ranging from 127 to 144 µm. As MCC content increased from 1 to 5%, Young’s modulus and tensile strength of the films improved significantly from 112.12 to 488.89 MPa and 3.21 to 11.18 MPa, respectively, while elongation at break decreased from 44.74 to 4.15%. Incorporating MCC also reduced film surface wettability, with the water contact angle increasing from 69.17° to 102.82°. The starch/3%MCC holds promise as a biocomposite film for medical packaging applications, offering advantages in terms of good transparency, mechanical properties, and surface hydrophobicity. Furthermore, the absence of microbial growth in the sterilized gauze pad with sealing in the sterilized starch/3%MCC film confirms that the UV-C sterilization, 30 min for each side at 254 nm effectively eliminated any microorganisms present on the starch/3%MCC film without damaging the film properties. This finding highlights a reliable approach to ensuring the sterility of starch/MCC films for medical packaging applications.
Full article
Open AccessArticle
Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm
by
Siarhei Autsou, Karolina Kudelina, Toomas Vaimann, Anton Rassõlkin and Ants Kallaste
Appl. Sci. 2024, 14(10), 4241; https://doi.org/10.3390/app14104241 - 16 May 2024
Abstract
The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates
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The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.
Full article
(This article belongs to the Collection Modeling, Design and Control of Electric Machines: Volume II)
Open AccessArticle
Identification and Optimization Study of Cavitation in High Power Torque Converter
by
Kaifeng Wang, Xiangyang Xu, Weiwei Zhao, Zhongshan Wang, Yulong Lei and Wenxing Ma
Appl. Sci. 2024, 14(10), 4240; https://doi.org/10.3390/app14104240 - 16 May 2024
Abstract
Aiming at the phenomenon that a high-power torque converter is susceptible to cavitation, which leads to performance degradation, first, a transient flow field model of the torque converter is established, and CFD simulation and experimental research on the torque converter are carried out
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Aiming at the phenomenon that a high-power torque converter is susceptible to cavitation, which leads to performance degradation, first, a transient flow field model of the torque converter is established, and CFD simulation and experimental research on the torque converter are carried out to find out the speed ratio region where cavitation occurs in the torque converter as well as the rule of occurrence of cavitation, and then the cavitation identification method based on the difference between the inlet and outlet flow of the torque converter is proposed. Then, the transient flow process inside the torque converter is analyzed, and it is pointed out that the angle between the inlet angle of the stator and the outlet angle of the turbine of the torque converter, i.e., the fluid inflow injection deviation angle is an important factor affecting the cavitation phenomenon. By adjusting the key parameters of the stator blade bone line, the fluid inflow deviation angle of the torque converter stator is optimized, so that the speed ratio range of cavitation under large load conditions is greatly reduced from the original 0–0.5 (50%) to 0–0.15 (15%). Meanwhile, in terms of test performance, the nominal torque of the torque converter is greatly improved under the premise of ensuring that the performance is basically unchanged, in which the nominal torque of the test zero speed is increased by 28.7%, and the cavitation of the torque converter has been greatly improved.
Full article
(This article belongs to the Collection Application of Computational Fluid Dynamics in Mechanical Engineering)
Open AccessArticle
Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things
by
Hanh Hong-Phuc Vo, Thuan Minh Nguyen and Myungsik Yoo
Appl. Sci. 2024, 14(10), 4239; https://doi.org/10.3390/app14104239 - 16 May 2024
Abstract
Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of
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Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.
Full article
Open AccessArticle
Frequency-Separated Attention Network for Image Super-Resolution
by
Daokuan Qu, Liulian Li and Rui Yao
Appl. Sci. 2024, 14(10), 4238; https://doi.org/10.3390/app14104238 - 16 May 2024
Abstract
The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep
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The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep networks are challenging to train, thus failing to exhibit the expressive capability commensurate with their depth. High-frequency and low-frequency features in images play different roles in image super-resolution. Networks based on CNNs, which should focus more on high-frequency features, treat these two types of features equally. This results in redundant computations when processing low-frequency features and causes complex and detailed parts of the reconstructed images to appear as smooth as the background. To maintain long-term memory and focus more on the restoration of image details in networks with strong representational capabilities, we propose the Frequency-Separated Attention Network (FSANet), where dense connections ensure the full utilization of multi-level features. In the Feature Extraction Module (FEM), the use of the Res ASPP Module expands the network’s receptive field without increasing its depth. To differentiate between high-frequency and low-frequency features within the network, we introduce the Feature-Separated Attention Block (FSAB). Furthermore, to enhance the quality of the restored images using heuristic features, we incorporate attention mechanisms into the Low-Frequency Attention Block (LFAB) and the High-Frequency Attention Block (HFAB) for processing low-frequency and high-frequency features, respectively. The proposed network outperforms the current state-of-the-art methods in tests on benchmark datasets.
Full article
(This article belongs to the Special Issue Advanced Pattern Recognition & Computer Vision)
Open AccessArticle
Study of the Influence of Heat Flow on the Time to Ignition of Spruce and Beech Wood
by
Jozef Harangozó, Ivana Tureková, Iveta Marková, Alena Hašková and Roman Králik
Appl. Sci. 2024, 14(10), 4237; https://doi.org/10.3390/app14104237 - 16 May 2024
Abstract
Adherence to fire safety regulations for wood is one of the most important tasks in its use in structural and architectural applications. This article deals with determining the influence of heat flux on the ignition process of spruce (Picea abies L. Karst.)
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Adherence to fire safety regulations for wood is one of the most important tasks in its use in structural and architectural applications. This article deals with determining the influence of heat flux on the ignition process of spruce (Picea abies L. Karst.) and beech wood (Fagus sylvatica L.). The heat flux was generated by an electric radiant panel. The analysed parameters included the ignition time of the spruce and beech wood samples, the influence of wood density, and sample moisture, and the course of sample combustion, both with and without flame, was observed. The heat flux was maintained at constant values, depending on the distance of the examined sample from the panel, along with the specific power of the radiation panel. The power of the radiation panel was set to constant values of 5 kW and 10 kW. The samples were placed at distances of 50, 70, 100, 150, and 200 mm from the heat source, and heat fluxes in the range of 13–92 kW·m−2 were observed. At a power of 5 kW and a heat flux of 64 kW·m−2, neither the sample of beech nor that of spruce wood, placed at the distance of 100 mm from the radiation panel, exhibited flaming combustion. The ignition time for the beech wood was approximately twice that of the spruce wood, likely due to the higher average wood density. It can be stated that wood density, as one of the main factors, significantly influences the ignition phase of burning. The statistical analysis examined variables including wood type, radiant panel output, distance, and heat flux in relation to ignition time. The analysis revealed a significant difference between ignition time and distance (p-value = 0.0000, H = 37.51583) as well as between ignition time and heat flux (p-value = 0.0000, H = 37.69726). Similarly, the time to ignition for all tested beech wood samples was longer than for spruce wood.
Full article
(This article belongs to the Special Issue Advanced Technologies in Environment Protection and Environmental Risk Assessment)
Open AccessArticle
Online Orientation Recognition of Single-Crystal Diamond Tools in the Process of Indexing Grinding Based on HMM and Multi-Information Fusion
by
Haitao Ma, Dayu Xia and Yifan Wu
Appl. Sci. 2024, 14(10), 4236; https://doi.org/10.3390/app14104236 - 16 May 2024
Abstract
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Single-crystal diamond tools occupy an important position in the field of optical processing as the basis and key to advanced optical manufacturing technology, such as grating manufacturing and optical mirror-turning processing. Single-crystal diamond tools have become the cornerstone of the development of related
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Single-crystal diamond tools occupy an important position in the field of optical processing as the basis and key to advanced optical manufacturing technology, such as grating manufacturing and optical mirror-turning processing. Single-crystal diamond tools have become the cornerstone of the development of related industries. This paper takes a single-crystal diamond arc tool as the research object. Sound signal analysis technology and vibration signal analysis technology are comprehensively applied to the online orientation identification of a single-crystal diamond tool in the indexing grinding process. The online orientation method of the tool is explored, the sound signal and the vibration signal are taken as the characteristic signals, and a wavelet algorithm (WT) is used to reduce the noise of the vibration signal and sound signal. The kurtosis of the sound signal and the kurtosis and skewness of the vibration signal in the high-order statistics strongly related to the grinding direction of a single-crystal diamond are used as the characteristic parameters, and the online direction recognition model of the tool is established using the Hidden Markov Method (HMM). The above characteristic parameters are used as model input for multi-information fusion. The mapping relationship between the characteristic parameters of the characteristic signal and the crystal orientation of the single-crystal diamond crystal face is obtained, and then the online orientation method of the single-crystal diamond arc tool in the process of indexing grinding is formed. The effectiveness of the method is verified by experiments, and effective orientation information is provided for research on the positioning control strategy of the tool grinding process to ensure the efficiency of grinding and improve the manufacturing level of the tool.
Full article
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Open AccessArticle
Micro- and Nano-Pollutants from Tires and Car Brakes Generated in the Winter Season in the Poznan City Urban Environment
by
Robert E. Przekop, Bogna Sztorch, Daria Pakuła, Eliza Romańczuk-Ruszuk, Roksana Konieczna and Miłosz Frydrych
Appl. Sci. 2024, 14(10), 4235; https://doi.org/10.3390/app14104235 - 16 May 2024
Abstract
This research, focusing on the environmental impact of tire and brake disc pad wear, constitutes a significant area of transport-related studies. These two key vehicle components are not only the most frequently worn but also generate micro- and nano-pollutants (i.e., rubber, metal
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This research, focusing on the environmental impact of tire and brake disc pad wear, constitutes a significant area of transport-related studies. These two key vehicle components are not only the most frequently worn but also generate micro- and nano-pollutants (i.e., rubber, metal oxides) that potentially harm the environment. Over half of the globally produced natural and synthetic rubbers, which amounted to about 30 million tons in 2022, are used for tire production. This work focuses on the study of roadside snow, sand, and standing water deposits from various locations in the urban agglomeration (Poznań, Poland) during the winter season, determining their qualitative composition and the quantitative content of pollutants originating from tire abrasion. In addition, the method of washing nano- and micro-rubber particles and their full characteristics was also presented. Fourier-transform infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopic studies, optical and scanning electron microscopy (SEM-EDS), particle size studies using a dynamic light scattering (DLS) particle analyzer, and thermogravimetric analysis (TGA) were conducted for a detailed characterization of the pollutants in the environment. The conducted particle separation methods allowed for the extraction of a fraction mainly containing gum residues with particle sizes less than 2 µm. The results of these tests make it possible to estimate the level of contamination with rubber and metal residues during the abrasion of tires, pads, and brake discs while driving, which is crucial for understanding the impact of vehicle part exploitation on the environment.
Full article
(This article belongs to the Section Green Sustainable Science and Technology)
Open AccessArticle
A Polarization-Based Method for Maritime Image Dehazing
by
Rui Ma, Zhenduo Zhang, Shuolin Zhang, Zhen Wang and Shuai Liu
Appl. Sci. 2024, 14(10), 4234; https://doi.org/10.3390/app14104234 - 16 May 2024
Abstract
The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes
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The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea–sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery.
Full article
Open AccessArticle
SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
by
Bin Guo, Ning Cao, Peng Yang and Ruihao Zhang
Appl. Sci. 2024, 14(10), 4233; https://doi.org/10.3390/app14104233 - 16 May 2024
Abstract
Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However,
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Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks.
Full article
(This article belongs to the Special Issue Applications of Computer Vision and Image Processing in Medicine)
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Open AccessReview
Nutritional and Medicinal Properties of Microbial Oil
by
Şuheda Uğur, Bartłomiej Zieniuk and Agata Fabiszewska
Appl. Sci. 2024, 14(10), 4232; https://doi.org/10.3390/app14104232 - 16 May 2024
Abstract
Plant and animal oils and fats currently dominate the edible oil market, but a new sustainable alternative of lipids from single-celled organisms has become advantageous in human nutrition and pharmacy. Single-cell oils (SCOs) are lipids biosynthesized and accumulated in the lipid bodies of
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Plant and animal oils and fats currently dominate the edible oil market, but a new sustainable alternative of lipids from single-celled organisms has become advantageous in human nutrition and pharmacy. Single-cell oils (SCOs) are lipids biosynthesized and accumulated in the lipid bodies of oleaginous species of bacteria, yeasts, molds, and algae. The review has investigated SCOs’ composition, with a detailed review of the described beneficial impact in medicine, cosmetics, pharmacy, and nutrition. Although microbial oil has been known for more than 100 years, it was not applied until the 21st century, when commercial SCO production for human use started and administrative regulations governing their use were completed. This article discusses the applications of SCOs, which can be easily found in microorganisms, in the pharmaceutical, cosmetic, and food industries. In addition, some aspects of 15- or 17-carbon-atom-long fatty acids were also pointed out. Furthermore, some challenges for heterotrophic single-cell oil synthesis and improvements in its extraction efficiency have also been concluded, which can further contribute to their broadened use in pharmacy, medicine, cosmetics, and food applications.
Full article
(This article belongs to the Special Issue Recent Developments in Bioactive Compounds Analysis for Food Quality Improvement)
Open AccessArticle
High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
by
S. M. F. D. Syed Mustapha
Appl. Sci. 2024, 14(10), 4231; https://doi.org/10.3390/app14104231 - 16 May 2024
Abstract
Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually
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Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). The proposed method is straightforward since it calculates 1-NN and Max-NN by analyzing the data point placements without the requirement of an initial manual parameter assignment. This method is validated using two well-known publicly available datasets used in several clustering algorithms. To compare the performance of the proposed method, this study also investigated four popular clustering algorithms (DBSCAN, affinity propagation, Mean Shift, and K-means), where the proposed method provides higher performance in finding the cluster without using any manually selected parameters. The experimental finding demonstrated that the proposed DPPA algorithm is less time-consuming compared to the existing traditional methods and achieves higher performance without using any manually selected parameters.
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(This article belongs to the Special Issue Artificial Intelligence and Digital Technology)
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Assessing the Stress Induced by Novel Packaging in GaN HEMT Devices via Raman Spectroscopy
by
Zainab Dahrouch, Giuliana Malta, Moreno d’Ambrosio, Angelo Alberto Messina, Mattia Musolino, Alessandro Sitta, Michele Calabretta and Salvatore Patanè
Appl. Sci. 2024, 14(10), 4230; https://doi.org/10.3390/app14104230 - 16 May 2024
Abstract
Micro-Raman spectroscopy was carried out to evaluate the localized residual stresses in commercial Gallium-Nitride-based devices, specifically, AlGaN/GaN high-electron-mobility Transistors (HEMTs) with a novel packaging design provided by STMicroelectronics S.r.l. (Catania, Italy). The packaging plays a key role in protecting the device core against
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Micro-Raman spectroscopy was carried out to evaluate the localized residual stresses in commercial Gallium-Nitride-based devices, specifically, AlGaN/GaN high-electron-mobility Transistors (HEMTs) with a novel packaging design provided by STMicroelectronics S.r.l. (Catania, Italy). The packaging plays a key role in protecting the device core against the external environment, thus minimizing damages caused by mechanical shocks, exposure to light, and contact with chemicals, conjointly achieving an efficient heat dissipation rate. Even though the packaging is a required step for the proper functioning of ready-to-use electronic devices, its application typically may introduce mechanical stress to AlGaN/GaN HEMTs, which can result in various reliability issues. In this paper, we investigate the impact of packaging on residual stress by analyzing the frequency shift of the E2 Raman peak along GaN layers and at the GaN/Si interface. An extensive evaluation was conducted using both a packaged device and a wafer-level device. The correlation between Raman frequency shifts of the E2 mode was accurately quantified, revealing a stress mitigation of approximately 0.1 GPa. This reduction is ascribed to the compressive stress introduced by the packaging, which partially offsets the intrinsic tensile stress of the wafer-level device. The proposed methodology could, in principle, be implemented to improve the development of packaging.
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(This article belongs to the Section Applied Physics General)
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Pulsed Propulsion of Unmanned Aerial Vehicles by Centrifugal Force Modulation—First-Order Theory and Practicability
by
Wolfgang Holzapfel
Appl. Sci. 2024, 14(10), 4229; https://doi.org/10.3390/app14104229 - 16 May 2024
Abstract
A novel technique suitable for the propulsion of small unmanned aerial vehicles (UAV) is discussed in this paper. This approach utilizes the rotational energy of airborne gyro rotors and converts it into translational propulsion for the vehicle. The energy conversion is achieved by
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A novel technique suitable for the propulsion of small unmanned aerial vehicles (UAV) is discussed in this paper. This approach utilizes the rotational energy of airborne gyro rotors and converts it into translational propulsion for the vehicle. The energy conversion is achieved by generating precisely directed centrifugal force pulses through short-duration rotor unbalances. The accurate control of the timing and magnitude of these unbalances is crucial for successful propulsion generation. Our first-order theory of controlled unbalance propulsion (CUP) predicts the potential for achieving high translational accelerations and vehicle velocities up to orbital levels. Power-saving levitation of UAVs can be attained. In this paper, we provide traceable evidence that pulsed centrifugal propulsion is based on well-established laws of physics and can be realized using state-of-the-art technologies.
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(This article belongs to the Section Aerospace Science and Engineering)
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Evaluation of the Effect of Primary and Secondary Closure on the Use of Leukocyte and Platelet-Rich Fibrin in Impacted Lower Third Molar Surgery
by
Zeynep Dilan Orhan, Levent Ciğerim, Mehmet Güzel, İbrahim Doğru, Mohammad Alsmadi, Nazlı Hilal Kahraman, Abdalrahim Hussein and Kader Tatar
Appl. Sci. 2024, 14(10), 4228; https://doi.org/10.3390/app14104228 - 16 May 2024
Abstract
The aim of this study was to compare the effect of using L-PRF in patients undergoing impacted lower third molar surgery with either primary or secondary closure techniques. Methods: This prospective, randomized, double-blind, split-mouth clinical trial was conducted on patients with bilateral impacted
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The aim of this study was to compare the effect of using L-PRF in patients undergoing impacted lower third molar surgery with either primary or secondary closure techniques. Methods: This prospective, randomized, double-blind, split-mouth clinical trial was conducted on patients with bilateral impacted lower third molars of a similar position. Primary closure was performed in group 1 and secondary closure in group 2. The group 1 closure technique was applied to one side of the patients, and the group 2 closure technique was applied to the other side at different times. Of the 45 patients evaluated, 9 patients were excluded from the study because of alveolitis and failure to attend regular control visits out. Results: Of the 36 patients included in the study, 23 were female and 13 were male, with a mean age of 22.42 ± 3.36 years. The secondary closure group had lower VAS scores at hour 6 (p < 0.05). Pain decreased more in the primary closure group when comparing changes between the VAS scores at 6 hours and 7 days (p < 0.05). Conclusions: The results of this study, showing that both secondary and primary closure are effective, with similar outcomes in terms of pain, swelling, and trismus, should be supported by future clinical trials.
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(This article belongs to the Section Applied Dentistry and Oral Sciences)
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