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
A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition
Appl. Sci. 2024, 14(11), 4742; https://doi.org/10.3390/app14114742 (registering DOI) - 30 May 2024
Abstract
Abstract: Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD)
[...] Read more.
Abstract: Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor–shaft–disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.
Full article
(This article belongs to the Special Issue Fault Diagnosis and Health Monitoring of Mechanical Systems)
Open AccessArticle
Dual-IoTID: A Session-Based Dual IoT Device Identification Model
by
Tao Zeng, Ke Ye, Fang Lou, Yue Chang, Mingyong Yin and Teng Hu
Appl. Sci. 2024, 14(11), 4741; https://doi.org/10.3390/app14114741 (registering DOI) - 30 May 2024
Abstract
The Internet of Things (IoT) is rapidly transforming our lives and work, enabling a wide range of emerging services and applications. However, as the scale of the IoT expands, its security issues are becoming increasingly prominent. Malicious actors can exploit vulnerabilities in IoT
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The Internet of Things (IoT) is rapidly transforming our lives and work, enabling a wide range of emerging services and applications. However, as the scale of the IoT expands, its security issues are becoming increasingly prominent. Malicious actors can exploit vulnerabilities in IoT devices to launch attacks. Protecting the IoT begins with device identification. Identified devices can have corresponding protective measures selected based on the information, thereby enhancing network security. In this study, we propose a dual-machine-learning-based IoT device identification algorithm, Dual-IoTID, which identifies devices based on the payload of IoT device sessions. In contrast to existing methods that rely on extracting header fields or network layer features, our approach attempts to obtain identification information from session payloads. Dual-IoTID first extracts frequent items from sessions and uses a first-layer classifier to obtain a confidence matrix for initial classification. Then, the confidence matrix, along with extracted session communication features, is fed into a second-layer classifier for IoT device identification. Our proposed method is applicable to any IoT device, and it is also suitable for networks with NAT enabled. Experimental results demonstrate that Dual-IoTID has higher accuracy than existing methods, achieving 99.48% accuracy in the UNSW dataset and accurately identifying IoT devices even in environments containing non-IoT devices.
Full article
(This article belongs to the Special Issue Adversarial Attacks and Cyber Security: Trends and Challenges)
Open AccessArticle
Investigation of the Tensile Properties of High-Strength Bolted Joints in Static Drill Rooted Nodular Piles
by
Zhongjin Wang, Qinyong Du, Rihong Zhang and Xinyu Xie
Appl. Sci. 2024, 14(11), 4740; https://doi.org/10.3390/app14114740 (registering DOI) - 30 May 2024
Abstract
To address the persistence of traditional welded joints in the construction of static drill rooted nodular piles, high-strength bolted connections are introduced. Tensile performance tests were conducted on seven sets of full-scale joint specimens to evaluate the ultimate tensile bearing capacity, deformation ductility,
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To address the persistence of traditional welded joints in the construction of static drill rooted nodular piles, high-strength bolted connections are introduced. Tensile performance tests were conducted on seven sets of full-scale joint specimens to evaluate the ultimate tensile bearing capacity, deformation ductility, and damage characteristics of high-strength bolted joints. Numerical models were established using ABAQUS 2020 software to complement the experimental findings. The results indicate that the ultimate tensile capacity test values of high-strength bolted joints and welded joints are comparable, both exceeding the values calculated by the pile ultimate tensile capacity specification formula. Moreover, the ultimate tensile capacity values of specimens with improved high-strength bolted joints surpass those of ordinary joints. Notably, in the final stages of testing, both high-strength bolted joints and welded joints experienced pull-off at the pier head of the prestressing reinforcement, with the joints remaining intact. The load-displacement curves obtained from the ABAQUS numerical model align closely with the experimental measurements. These findings offer valuable insights and serve as an experimental foundation for promoting the adoption and utilization of high-strength bolt joints.
Full article
Open AccessArticle
Research on Tree Flash Fault Localization of Hybrid Overhead–Underground Lines Based on Improved Double-Ended Traveling Wave Method
by
Zukang Huang, Chunhua Fang, Quancai Jiang, Tao Hu and Junjie Lv
Appl. Sci. 2024, 14(11), 4739; https://doi.org/10.3390/app14114739 (registering DOI) - 30 May 2024
Abstract
The occurrence of tree flash faults in hybrid overhead–underground lines presents a significant challenge to the smooth operation of power systems. However, research on localizing such faults is relatively scarce. This study conducts theoretical analyses on the formation of tree flash faults, constructs
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The occurrence of tree flash faults in hybrid overhead–underground lines presents a significant challenge to the smooth operation of power systems. However, research on localizing such faults is relatively scarce. This study conducts theoretical analyses on the formation of tree flash faults, constructs a tree flash fault discharge test platform, and simulates the discharge process. The tree flash fault discharge traveling wave signals were obtained through a high-frequency current acquisition system. Additionally, this paper establishes a model for the current traveling wave of tree flash faults and analyzes transmission attenuation. To enhance the bi-terminal traveling wave localization method, we introduce modal decomposition and the Hilbert–Huang transform. Modal decomposition is used to disentangle signals and derive the instantaneous frequencies of modal signal components through the Hilbert–Huang transform. This process helps determine the time at which the initial wavefront reaches the terminals of the mixed-line transmission. The simulation analysis carried out using PSCAD/EMTDC v4.6.3 demonstrates that this method effectively calibrates the wavefront timing of tree flash fault signals without requiring knowledge of their wave velocity along the mixed-line transmission. Therefore, this approach achieves precise localization of tree flash faults efficiently.
Full article
(This article belongs to the Topic Power System Protection)
Open AccessArticle
Improving Supply Chain Management Processes Using Smart Contracts in the Ethereum Network Written in Solidity
by
Eren Yigit and Tamer Dag
Appl. Sci. 2024, 14(11), 4738; https://doi.org/10.3390/app14114738 (registering DOI) - 30 May 2024
Abstract
This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their
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This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their integration into supply chains to increase traceability, transparency, and accountability with faster processing times. After investigating these technologies’ applications and potential use cases, a framework for smart contract implementation for supply chain management is constructed. Potential data models and functions of a smart contract implementation improving supply chain management processes are discussed. After constructing a framework, the effects of the proposed system on supply chain processes are explained. The proposed framework increases the reliability of the supply chain history due to the usage of DLT (distributed ledger technology). It utilizes smart contracts to increase the manageability and traceability of the supply chain. The proposed framework also eliminates the SPoF (Single Point of Failure) vulnerabilities and external alteration of the transactional data. However, due to the ever-changing and variable nature of the supply chains, the proposed architecture might not be a one-size-fits-all solution, and tailor-made solutions might be necessary for different supply chain management implementations.
Full article
(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)
Open AccessArticle
Shear Reinforcement Effectiveness of One-Way Void Slab with the Hollow Core Ratio and Shear Reinforcement
by
Seungho Cho, Seunguk Na and Jungsoo Ha
Appl. Sci. 2024, 14(11), 4737; https://doi.org/10.3390/app14114737 (registering DOI) - 30 May 2024
Abstract
Void slabs offer a promising solution for sustainable construction due to their reduced weight and potential for recycled materials. However, their inherent hollowness can compromise shear capacity compared to solid slabs. This study investigates the effectiveness of shear reinforcement in mitigating this vulnerability.
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Void slabs offer a promising solution for sustainable construction due to their reduced weight and potential for recycled materials. However, their inherent hollowness can compromise shear capacity compared to solid slabs. This study investigates the effectiveness of shear reinforcement in mitigating this vulnerability. Experimental testing with a four-point support loading confirmed shear failure in all specimens and revealed a significant reserve of shear strength exceeding predictions from ACI 318-14 by at least 1.436. This suggests the potential for more efficient designs that utilize less shear reinforcement while maintaining structural integrity. An inverse relationship between porosity and shear strength was observed, highlighting the importance of considering void content during design. Among established design codes (ACI 318-14, UBC 2, and CEB-FIP 1990), CEB-FIP 1990 provided the most accurate prediction of shear capacity for these reinforced hollow slabs. These findings offer valuable insights for optimizing the shear design of voided slabs. The observed strength reserve suggests the potential for reduced shear reinforcement while maintaining safety. Additionally, the influence of porosity and the code comparison provide crucial considerations for future design practices. This research paves the way for developing efficient and safe voided slab applications, promoting sustainability in the construction industry.
Full article
Open AccessArticle
A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising
by
Yuhang Geng, Shaoping Xu, Minghai Xiong, Qiyu Chen and Changfei Zhou
Appl. Sci. 2024, 14(11), 4735; https://doi.org/10.3390/app14114735 (registering DOI) - 30 May 2024
Abstract
While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further
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While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Gender and Accent Biases in AI-Based Tools for Spanish: A Comparative Study between Alexa and Whisper
by
Eduardo Nacimiento-García, Holi Sunya Díaz-Kaas-Nielsen and Carina S. González-González
Appl. Sci. 2024, 14(11), 4734; https://doi.org/10.3390/app14114734 (registering DOI) - 30 May 2024
Abstract
Considering previous research indicating the presence of biases based on gender and accent in AI-based tools such as virtual assistants or automatic speech recognition (ASR) systems, this paper examines these potential biases in both Alexa and Whisper for the major Spanish accent groups.
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Considering previous research indicating the presence of biases based on gender and accent in AI-based tools such as virtual assistants or automatic speech recognition (ASR) systems, this paper examines these potential biases in both Alexa and Whisper for the major Spanish accent groups. The Mozilla Common Voice dataset is employed for testing, and after evaluating tens of thousands of audio fragments, descriptive statistics are calculated. After analyzing the data disaggregated by gender and accent, it is observed that, for this dataset, in terms of means and medians, Alexa performs slightly better for female voices than for male voices, while the opposite is true for Whisper. However, these differences in both cases are not considered significant. In the case of accents, a higher Word Error Rate (WER) is observed among certain accents, suggesting bias based on the spoken Spanish accent.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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Open AccessArticle
Differentiated Interval Structural Characteristics of Wufeng−Longmaxi Formation Deep Shale Gas Reservoirs in Western Chongqing Area, China: Experimental Investigation Based on Low-Field Nuclear Magnetic Resonance (NMR) and Fractal Modeling
by
Difei Zhao, Dandan Liu, Yuan Wei, Qinxia Wang, Shengxiu Wang, Xiaoyu Zou, Weiwei Jiao, Yinghai Guo and Geoff Wang
Appl. Sci. 2024, 14(11), 4733; https://doi.org/10.3390/app14114733 (registering DOI) - 30 May 2024
Abstract
The study of deep shale gas (>3500 m) has become a new research hotspot in the field of shale gas research in China. In this study, 16 representative deep shale samples were selected from different layers of the Wufeng–Longmaxi Formation in the Z-3
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The study of deep shale gas (>3500 m) has become a new research hotspot in the field of shale gas research in China. In this study, 16 representative deep shale samples were selected from different layers of the Wufeng–Longmaxi Formation in the Z-3 well in the western Chongqing area to conduct low-field nuclear magnetic resonance (NMR) tests, field-emission scanning electron microscopy (FE-SEM) observation, and fractal modeling. By comparing the differences in pore structure and their influencing factors in representative samples from different layers, the particularities of high-quality reservoirs have been revealed. The results show that the Z-3 well shales mainly develop micropores and mesopores, with pore sizes of 1 nm–200 nm. The fractal dimensions of bound fluid pores D1 (1.6895–2.3821) and fractal dimension of movable fluid pores D2 (2.9914–2.9996) were obtained from T2 spectra and linear fitting, and the pores were divided into three sections based on the NMR fractal characteristics. TOC content was one of the major factors affecting the gas content in the study area. The shale samples in the bottom S1l1-1 sub-layer with a higher TOC content have larger porosity and permeability, leading to enhanced homogeneity of the pore structure and favorable conditions for shale gas adsorption. A comparative understanding of the particularities of pore structure and influencing factors in high-quality reservoirs with higher gas content will provide the scientific basis for further exploration and exploitation of the Wufeng–Longmaxi Formation deep shale reservoirs in the western Chongqing area.
Full article
(This article belongs to the Special Issue State-of-the-Art Earth Sciences and Geography in China)
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Open AccessArticle
Study on Local Vibration Control of the 100 m X-BOW Polar Exploration Cruise Ship
by
Guohe Jiang, Jiachen Chen, Hao Guo, Gang Wu and Zhenzhen Liu
Appl. Sci. 2024, 14(11), 4732; https://doi.org/10.3390/app14114732 (registering DOI) - 30 May 2024
Abstract
A finite element model of a 100 m X-BOW polar exploration cruise ship has been developed. The ship’s frequency response analysis was conducted, with the simulated results closely matching the test data. The maximum discrepancy was 22%, equating to a negligible 0.24 mm/s
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A finite element model of a 100 m X-BOW polar exploration cruise ship has been developed. The ship’s frequency response analysis was conducted, with the simulated results closely matching the test data. The maximum discrepancy was 22%, equating to a negligible 0.24 mm/s difference in terms of comfort. This indicates that the simulation meets the standards of engineering precision and validates the model’s accuracy. Utilizing a global modal equivalent mass solution approach, in conjunction with the spatial distribution of local modal mass, a method for calculating the equivalent mass of a single local mode in mixed modes has been devised. This method was applied to determine the equivalent mass of the local vibration region of the 100 m X-BOW ship. Tuned mass dampers (TMDs) were then designed based on this equivalent mass. Analysis reveals that the TMDs achieve a 31 dB vibration absorption effect at a frequency of 13.4 Hz with a mass ratio of 0.05. They also provide a control effect at 10 Hz and 18.8 Hz, corresponding to 3 dB and 2 dB reductions, respectively. The control frequency band is broad, flat, and robust, indicating the effectiveness of the TMDs in mitigating vibrations across a wide range of frequencies.
Full article
(This article belongs to the Section Marine Science and Engineering)
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Open AccessArticle
Fragility Analysis of Step-Terrace Frame-Energy Dissipating Rocking Wall Structure in Mountain Cities
by
Youfa Yang, Yingwei Jia and Hongshen Jin
Appl. Sci. 2024, 14(11), 4731; https://doi.org/10.3390/app14114731 (registering DOI) - 30 May 2024
Abstract
Rocking walls can control the overall deformation pattern and the distribution of plastic energy dissipation in structures, suppressing the occurrence of weak layers. In the case of step-terrace frame structures, issues such as severe lateral stiffness irregularities, abrupt changes in floor-bearing capacity, and
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Rocking walls can control the overall deformation pattern and the distribution of plastic energy dissipation in structures, suppressing the occurrence of weak layers. In the case of step-terrace frame structures, issues such as severe lateral stiffness irregularities, abrupt changes in floor-bearing capacity, and concentrated deformation in upper ground layers exist. To improve the yielding and failure modes of step-terrace frame structures in mountainous regions, this paper proposes a structural system combining step-terrace frame structures with energy dissipation rocking walls attached to their bottoms, aiming to control the yielding mechanism of the structure, further reduce the seismic response, limit residual deformation, and propose a structural system of step-terrace frame structures with buckling-restrained braces (BRBs) and energy dissipation rocking walls. Two sets of numerical models for step-terrace frame structures with different numbers of dropped layers and spans were established. Through simulating low-cycle repeated loading tests on step-terrace frame structures, the rationality of the models and parameters was verified. Incremental dynamic analysis (IDA) was employed to systematically investigate the vulnerability of step-terrace frame structures with energy dissipation rocking walls under different dropped layer and span configurations. This investigation covered aspects such as IDA curve clusters, percentile curves, seismic demand models, fragility functions, failure state probabilities, vulnerability indices, collapse resistance factors, and safety margins. The results indicated that the change in dropped layer numbers had a far greater impact on the vulnerability of step-terrace frame structures with energy dissipation rocking walls than the change in dropped span numbers. Under seismic excitations with the same peak ground acceleration (PGA), rocking walls can limit the depth of structural plasticity development, reduce the dispersion of peak responses, and lower the probability of exceeding various performance levels, thereby exhibiting good collapse resistance. The addition of buckling-restrained braces (BRBs) can further enhance the seismic performance and collapse resistance of the rocking wall frame structure. By analyzing the correlation between seismic intensity measures and peak structural responses, the validity of using peak ground acceleration as a scaling indicator for incremental dynamic analysis (IDA) has been verified.
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(This article belongs to the Section Civil Engineering)
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Open AccessArticle
Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features
by
Jarosław Kurek, Elżbieta Świderska and Karol Szymanowski
Appl. Sci. 2024, 14(11), 4730; https://doi.org/10.3390/app14114730 (registering DOI) - 30 May 2024
Abstract
The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very
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The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges.
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(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)
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Open AccessArticle
A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments
by
Sarah A. Algethami and Sultan S. Alshamrani
Appl. Sci. 2024, 14(11), 4729; https://doi.org/10.3390/app14114729 (registering DOI) - 30 May 2024
Abstract
The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore,
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The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives.
Full article
(This article belongs to the Special Issue Security in Internet of Things (IoT): Challenges, Solutions and Future Directions)
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Open AccessReview
A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
by
Ahmed Zentani, Ali Almaktoof and Mohamed T. Kahn
Appl. Sci. 2024, 14(11), 4728; https://doi.org/10.3390/app14114728 (registering DOI) - 30 May 2024
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Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast
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Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.
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Open AccessCase Report
Adsorption on Powdered Activated Carbon (PAC) Dosed into an Anthracite-Sand Filter in Water Treatment—Model and Criterion Equations
by
Andrzej Bielski and Jakub Ożóg
Appl. Sci. 2024, 14(11), 4727; https://doi.org/10.3390/app14114727 (registering DOI) - 30 May 2024
Abstract
This paper presents research on the mass dispersion and adsorption of organics present in tap water on powdered activated carbon (PAC) in a two-layer filter column. The adsorption rate depends on the difference between the concentration of organics and the equilibrium concentration. In
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This paper presents research on the mass dispersion and adsorption of organics present in tap water on powdered activated carbon (PAC) in a two-layer filter column. The adsorption rate depends on the difference between the concentration of organics and the equilibrium concentration. In homogeneous flocculators with simultaneous adsorption on PAC, the concentration difference is lower than in a filter column with PAC. Therefore, the utilization of the PAC’s adsorption capacity in filters is higher than in homogeneous flocculators. PAC is introduced into the upper anthracite layer of a filter bed, while the bottom layer is a sand layer, which protects the underdrain system from becoming clogged with PAC particles. The sorbent wis introduced into the bed in the final phase of filter backwashing. The authors present a model of adsorption on PAC in a filter column. Both experiments and calculations confirmed a better utilization of PAC’s adsorption capacity in the filter column compared to its utilization in a homogeneous flocculator. Three criterion equations were developed using dimensionless numbers, Re, Pe and Nu, as well as two similarity moduli related to a sorbent apparent density and an adsorption coefficient. Additionally, a relationship between the Peclet number (Pe) and the Reynolds number (Re) as well as the similarity modulus for the sorbent apparent density were determined for the mass dispersion process. The relationship between the diffusive Nuselt number (Nu) and the Re number as well as the similarity modulus for the sorbent apparent density were determined for the parameter describing an adsorbate permeation rate across a water–sorbent interface. The impact of the Re number and the similarity modulus for the sorbent apparent density on the Henry constant was also investigated. The criterion equations can be used to determine the adsorption model parameters; they may be helpful in designing a filtration system supplemented with PAC. In the capillary velocity range ∈ ⟨0.15·10−2; 0.72·10−2⟩ m/s and with a change in the apparent density of the sorbent from 3000 to 12,000 g PAC/m3 of the bed, as a result of the experimental tests carried out, it was established that the actual coefficient of longitudinal dispersion varied in the range of 0.16·10−4 to 2.03·10−4 m2/s, the product of the constant mass transfer rate and the specific outer surface of sorbent varied in the range of 2.23·10−7 to 1.70·10−6 (m/s)·(m2/g PAC), while the Henry constant Γ* varied in the range of 7.24 to 44.20 1/m3 of sorbent and the Henry constant Γ varied in the range of 0.0012 to 0.0019 m3 of water/g PAC.
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(This article belongs to the Section Environmental Sciences)
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Open AccessArticle
Design of a Dispersive 1064 nm Fiber Probe Raman Imaging Spectrometer and Its Application to Human Bladder Resectates
by
Juan David Muñoz-Bolaños, Tanveer Ahmed Shaik, Arkadiusz Miernik, Jürgen Popp and Christoph Krafft
Appl. Sci. 2024, 14(11), 4726; https://doi.org/10.3390/app14114726 (registering DOI) - 30 May 2024
Abstract
This study introduces a compact Raman spectrometer with a 1064 nm excitation laser coupled with a fiber probe and an inexpensive motorized stage, offering a promising alternative to widely used Raman imaging instruments with 785 nm excitation lasers. The benefits of 1064 nm
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This study introduces a compact Raman spectrometer with a 1064 nm excitation laser coupled with a fiber probe and an inexpensive motorized stage, offering a promising alternative to widely used Raman imaging instruments with 785 nm excitation lasers. The benefits of 1064 nm excitation for biomedical applications include further suppression of fluorescence background and deeper tissue penetration. The performance of the 1064 nm instrument in detecting cancer in human bladder resectates is demonstrated. Raman images with 1064 nm excitation were collected ex vivo from 10 human tumor and non-tumor bladder specimens, and the results are compared to previously published Raman images with 785 nm excitation. K-Means cluster (KMC) analysis is used after pre-processing to identify Raman signatures of control, tumor, necrosis, and lipid-rich tissues. Hierarchical cluster analysis (HCA) groups the KMC centroids of all specimens as input. The tools for data processing and hyperspectral analysis were compiled in an open-source Python library called SpectraMap (SpMap). In spite of lower spectral resolution, the 1064 nm Raman instrument can differentiate between tumor and non-tumor bladder tissues in a similar way to 785 nm Raman spectroscopy. These findings hold promise for future clinical hyperspectral Raman imaging, in particular for specimens with intense fluorescence background, e.g., kidney stones that are discussed as another widespread urological application.
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(This article belongs to the Special Issue Spectroscopic Techniques in Biomedical Imaging and Analysis)
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Open AccessArticle
Thermo-Regulated Cotton: Enhanced Insulation through PVA Nanofiber-Coated PCM Microcapsules
by
Dilara Dirlik-Uysal, David Mínguez-García, Eva Bou-Belda, Jaime Gisbert-Payá and Marilés Bonet-Aracil
Appl. Sci. 2024, 14(11), 4725; https://doi.org/10.3390/app14114725 (registering DOI) - 30 May 2024
Abstract
The innovative integration of phase change materials (PCMs) into textiles through microencapsulation presents a transformative approach to developing thermally regulated fabrics. This study explores the synthesis and characterization of microcapsules containing a coconut oil core and an ethylcellulose shell, and their application on
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The innovative integration of phase change materials (PCMs) into textiles through microencapsulation presents a transformative approach to developing thermally regulated fabrics. This study explores the synthesis and characterization of microcapsules containing a coconut oil core and an ethylcellulose shell, and their application on cotton fabrics coated with polyvinyl alcohol (PVA) nanofibers. The dual-layer system involving microcapsules and nanofibers is designed to enhance the thermal insulation properties of textiles by regulating heat through the absorption and release of thermal energy. The microencapsulation of PCMs allows for the effective incorporation of these materials into textiles without altering the fabric’s inherent properties. In this study, the coconut oil serves as the PCM, known for its suitable phase change temperature range, while ethylcellulose provides a robust shell, enhancing the microcapsules’ structural integrity. The application of a PVA nanofibers layer not only strengthens the thermal regulation properties but also protects the microcapsules from release while the fabric is manipulated, thereby prolonging the functional life of the fabric. Comprehensive testing, including scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), confirms the successful application and durability of the microcapsules on the textiles. Thermal imaging studies demonstrate the fabric’s enhanced capability to maintain a consistent temperature, highlighting the potential of this technology in applications ranging from smart clothing to energy-efficient building materials or automotive isolation. The integration of PCMs in textiles via microencapsulation and nanofiber technology marks a significant advancement in textile engineering, offering new opportunities for the development of smart and sustainable materials. The study demonstrates the promising potential of integrating PCMs into textiles using microencapsulation and nanofiber technologies. Despite the initially modest insulation improvements, the methodology provides a robust foundation for further research and development.
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(This article belongs to the Special Issue Green Insulating Materials for Automotive, Construction, and Industrial Applications)
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Open AccessArticle
DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
by
Yifan Huang, Zhiwen Huang and Tao Jin
Appl. Sci. 2024, 14(11), 4724; https://doi.org/10.3390/app14114724 (registering DOI) - 30 May 2024
Abstract
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Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency
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Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.
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Open AccessArticle
Graph-Driven Exploration of Issue Handling Schemes in Software Projects
by
Bartosz Dobrzyński and Janusz Sosnowski
Appl. Sci. 2024, 14(11), 4723; https://doi.org/10.3390/app14114723 (registering DOI) - 30 May 2024
Abstract
The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed
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The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed to classical data mining of software repositories, we consider fine-grained features of issues which provide a better insight into project evolution. A thorough analysis of repository contents allows us to define useful metrics for characterizing issue handling schemes. These metrics are derived from the introduced graph model and developed original data mining algorithms targeting timing, issue flow progress and project actor activity aspects. This study is associated with issue processing states and their sequences (handling paths), leading to problem resolution. The introduced taxonomy of issue processing schemes facilitates the creation of a pertinent knowledge database and the identification of both bad (anomalies) and good practices. The proposed approach is illustrated with experimental results related to a representative set of ITS project repositories. These results enhance experts’ knowledge of the project and can be used for correct decision-making actions. They reveal weak points in project development and possible directions for improvement.
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(This article belongs to the Special Issue Knowledge and Data Engineering)
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An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
by
Micheal Dutt, Aditya Gupta, Morten Goodwin and Christian W. Omlin
Appl. Sci. 2024, 14(11), 4722; https://doi.org/10.3390/app14114722 (registering DOI) - 30 May 2024
Abstract
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Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This
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Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system’s explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB+D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results.
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