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
Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network
Appl. Sci. 2024, 14(11), 4885; https://doi.org/10.3390/app14114885 (registering DOI) - 5 Jun 2024
Abstract
Light field (LF) cameras can capture a scene’s information from all different directions and provide comprehensive image information. However, the resulting data processing commonly encounters problems of low contrast and low image quality. In this article, we put forward a content-adaptive light field
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Light field (LF) cameras can capture a scene’s information from all different directions and provide comprehensive image information. However, the resulting data processing commonly encounters problems of low contrast and low image quality. In this article, we put forward a content-adaptive light field contrast enhancement scheme using a focal stack (FS) and hierarchical structure. The proposed FS set contained 300 light field images, which were captured using a Lytro-Illum camera. In addition, we integrated the classical Stanford Lytro Light Field Archive and JPEG Pleno Database. Specifically, according to the global brightness, the acquired LF images were classified into four different categories. First, we transformed the original LF FS into a depth map (DMAP) and all-in-focus (AIF) image. The image category was preliminarily determined depending on the brightness information. Then, the adaptive parameters were acquired by the corresponding multilayer perceptron (MLP) network training, which intrinsically enhanced the contrast and adjusted the light field image. Finally, our method automatically produced an enhanced FS based on the DMAP and AIF image. The experimental comparison results demonstrate that the adaptive values predicted by our MLP had high precision and approached the ground truth. Moreover, compared to existing contrast enhancement methods, our method provides a global contrast enhancement, which improves, without over-enhancing, local areas. The complexity of image processing is reduced, and real-time, adaptive LF enhancement is realized.
Full article
(This article belongs to the Special Issue Advances and Application of Intelligent Video Surveillance Systems: Volume II)
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Open AccessArticle
On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
by
Zan Zhang, Beiju Huang, Zanyun Zhang and Hongda Chen
Appl. Sci. 2024, 14(11), 4886; https://doi.org/10.3390/app14114886 (registering DOI) - 4 Jun 2024
Abstract
In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs).
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In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios.
Full article
(This article belongs to the Special Issue Nanophotonics and Integrated Photonics)
Open AccessArticle
Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
by
Xiaolin Xie, Zixiang Yan, Zhihong Zhang, Yibo Qin, Hang Jin, Cheng Zhang and Man Xu
Appl. Sci. 2024, 14(11), 4884; https://doi.org/10.3390/app14114884 (registering DOI) - 4 Jun 2024
Abstract
Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to
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Traditional automatic gardening pruning robots generally employ electronic fences for the delineation of working boundaries. In order to quickly determine the working area of a robot, we combined an improved DeepLabv3+ semantic segmentation model with a simultaneous localization and mapping (SLAM) system to construct a three-dimensional (3D) semantic map. To reduce the computational cost of its future deployment in resource-constrained mobile robots, we replaced the backbone network of DeepLabv3+, ResNet50, with MobileNetV2 to decrease the number of network parameters and improve recognition speed. In addition, we introduced an efficient channel attention network attention mechanism to enhance the accuracy of the neural network, forming an improved Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED) network model. Through the integration of this model with the SLAM system, the entire framework was able to generate a 3D semantic point cloud map of a lawn working area and convert it into octree and occupancy grid maps, providing technical support for future autonomous robot operation and navigation. We created a lawn dataset containing 7500 images, using our own annotated images as ground truth. This dataset was employed for experimental purposes. Experimental results showed that the proposed MM-ED network model achieved 91.07% and 94.71% for MIoU and MPA metrics, respectively. Using a GTX 3060 Laptop GPU, the frames per second rate reached 27.69, demonstrating superior recognition performance compared to similar semantic segmentation architectures and better adaptation to SLAM systems.
Full article
(This article belongs to the Special Issue Advanced 2D/3D Computer Vision Technology and Applications)
Open AccessArticle
Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data
by
Dong-Yoon Kim, Hyung Won Lee, Jiyoung Yu and Jong-Kyu Park
Appl. Sci. 2024, 14(11), 4883; https://doi.org/10.3390/app14114883 (registering DOI) - 4 Jun 2024
Abstract
For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through
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For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove butt joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data.
Full article
(This article belongs to the Special Issue Advanced Manufacturing and Nondestructive Testing Techniques)
Open AccessArticle
An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
by
Wei Zhao, Yujin Zhang, Yongqi Wang and Shiwen Zhang
Appl. Sci. 2024, 14(11), 4882; https://doi.org/10.3390/app14114882 - 4 Jun 2024
Abstract
In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set,
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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0–6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%.
Full article
(This article belongs to the Special Issue Deep Learning for Speech, Image and Language Processing)
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Open AccessArticle
Beyond Sight: Enhancing Augmented Reality Interactivity with Audio-Based and Non-Visual Interfaces
by
Jingya Li
Appl. Sci. 2024, 14(11), 4881; https://doi.org/10.3390/app14114881 - 4 Jun 2024
Abstract
Augmented Reality (AR) is rapidly advancing, with a new focus on broadening accessibility beyond the visually dominant interfaces. This study explores the integration of audio-based non-visual interfaces within AR, aiming to cater to a diverse audience, including users with visual impairments. The objective
[...] Read more.
Augmented Reality (AR) is rapidly advancing, with a new focus on broadening accessibility beyond the visually dominant interfaces. This study explores the integration of audio-based non-visual interfaces within AR, aiming to cater to a diverse audience, including users with visual impairments. The objective was to develop a prototype that leverages audio feedback to facilitate interaction with the AR environment, enhancing spatial awareness and mental imagery for all users without relying on visual cues. Employing a user-centered design approach, we conducted a comprehensive evaluation with university students to assess the prototype’s usability and immersive potential compared to traditional touchscreen interfaces. The findings highlighted a pronounced preference for the Audio-Based Natural Interface, emphasizing its capacity to provide an intuitive and immersive AR experience through sound alone. These results underline the potential of audio feedback in creating more inclusive AR experiences, suggesting a paradigm shift towards developing AR technologies that are accessible to a wider user base. Our study concludes that audio-based non-visual interfaces represent a viable and innovative direction for AR development, advocating for their further exploration to ensure AR’s universality and inclusivity.
Full article
(This article belongs to the Special Issue Virtual/Augmented Reality and Its Applications)
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Open AccessArticle
Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training
by
Alexandre M. Nascimento, Gabriel Kenji G. Shimanuki and Luiz Alberto V. Dias
Appl. Sci. 2024, 14(11), 4880; https://doi.org/10.3390/app14114880 - 4 Jun 2024
Abstract
As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of
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As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort.
Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
Open AccessArticle
Oncom from Surplus Bread Enriched in Vitamin B12 via In Situ Production by Propionibacterium freudenreichii
by
Bożena Stodolak, Anna Starzyńska-Janiszewska and Dagmara Poniewska
Appl. Sci. 2024, 14(11), 4879; https://doi.org/10.3390/app14114879 - 4 Jun 2024
Abstract
Bread is a frequently wasted food product. Surplus or stale bread can be successfully processed by solid-state fermentation and used as the only fermentation substrate. Oncom, which originated in Indonesia, is made with moulds of the Neurospora genus. This experiment aimed to obtain
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Bread is a frequently wasted food product. Surplus or stale bread can be successfully processed by solid-state fermentation and used as the only fermentation substrate. Oncom, which originated in Indonesia, is made with moulds of the Neurospora genus. This experiment aimed to obtain oncome from stale bread enriched in vitamin B12. Co-fermentation with N. sitophila and Propionibacterium freudenreichii was carried out on two types of bread differing in chemical composition and initial pH value. Oncom obtained after 5 days of fermentation, depending on the substrate used and the fermentation variant (fungal, fungal-bacterial), contained from 35 to 40% dry mass, from 17.5 to about 23% protein, about 2 to max 5% fat, and from 65 to 74% carbohydrates by weight in dry mass. Vitamin B12 content depended largely on the bacterial strain, the colony-forming unit dose in the inoculum, and also the initial pH of the substrate. The oncom product obtained after co-fermentation with P. freudenreichii DSM 20271 contained a maximum of 1.3 µg/100 g, which corresponds to the vitamin B12 level in a chicken egg.
Full article
(This article belongs to the Special Issue Bioactive Compounds and Enriched Foods: Technological and Nutritional Aspects)
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Open AccessArticle
A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
by
Xuanyu Zheng, Changsheng Zhang, Yang An and Bin Zhang
Appl. Sci. 2024, 14(11), 4878; https://doi.org/10.3390/app14114878 - 4 Jun 2024
Abstract
Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At
[...] Read more.
Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.
Full article
(This article belongs to the Special Issue Exploration and Application of Swarm Intelligence and Evolutionary Computation)
Open AccessReview
Plant Synthetic Promoters
by
Piotr Szymczyk and Małgorzata Majewska
Appl. Sci. 2024, 14(11), 4877; https://doi.org/10.3390/app14114877 - 4 Jun 2024
Abstract
This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter
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This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dTALEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops.
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(This article belongs to the Section Applied Biosciences and Bioengineering)
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Open AccessArticle
Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications
by
Xu Cui, Peng Yu, Yan Liu, Yinghui Wang and Jin Guo
Appl. Sci. 2024, 14(11), 4876; https://doi.org/10.3390/app14114876 - 4 Jun 2024
Abstract
With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources.
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With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.
Full article
(This article belongs to the Special Issue Statistical Signal Processing: Theory, Methods and Applications)
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Open AccessArticle
Noise Analysis and Structural Optimization of Automobile Scroll Compressor Air Valve
by
Feng Gao, Bin Yang, Xin Li and Jinguo Wu
Appl. Sci. 2024, 14(11), 4875; https://doi.org/10.3390/app14114875 - 4 Jun 2024
Abstract
The air conditioning compressor is a critical component in automobile heating, ventilation and air conditioning systems. However, compressor noise has long been a problem for automobile manufacturers. In recent years, the development and application of automobile air conditioning scroll compressors has increased significantly
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The air conditioning compressor is a critical component in automobile heating, ventilation and air conditioning systems. However, compressor noise has long been a problem for automobile manufacturers. In recent years, the development and application of automobile air conditioning scroll compressors has increased significantly due to their low mechanical vibration and noise. However, their limitations in terms of airflow pulse and noise cannot be ignored, especially in low speed and high load conditions where the noise generated has a negative impact on driving and passenger experience. Noise and airflow pulses are important considerations that cannot be ignored. This study innovatively modifies the end cap structure of the scroll compressor, using the principles of expansion muffler and insertion tube structure, with the aim of improving the acoustic quality of the scroll compressor. The results show that the novel valve construction can significantly reduce the sound pressure level of the scroll compressor noise to a maximum of 75.20 dBA. The results of this study provide a theoretical basis and practical technical applications for future research and development in the automobile industry.
Full article
(This article belongs to the Special Issue High-Energy Performance Compressors: Advanced Technologies and Applications)
Open AccessArticle
Digital Product Passport Implementation Based on Multi-Blockchain Approach with Decentralized Identifier Provider
by
Mihai Hulea, Radu Miron and Vlad Muresan
Appl. Sci. 2024, 14(11), 4874; https://doi.org/10.3390/app14114874 - 4 Jun 2024
Abstract
This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union’s circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life
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This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union’s circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life instructions to improve recycling practices and promote sustainable consumption. The approach integrates decentralized identifier (DID) technology for unique product identification using the cheqd.io platform with an enterprise tailored Hyperledger Fabric blockchain network for DPP data management, leveraging their strengths to enhance security and efficiency. This paper details the data model for the DPP, including entities like Product, Manufacturer, Supplier, and Material. Performance tests on the Hyperledger Fabric network demonstrate the system’s efficacy, focusing on CRUD operations and scalability. Future work will extend to the development of client applications and more comprehensive performance evaluations considering scalability and network expansion.
Full article
(This article belongs to the Special Issue Manufacturing Sustainability in a Circular Economy)
Open AccessArticle
Red Potato Pulp and Cherry Pomace for Pasta Enrichment: Health-Promoting Compounds, Physical Properties and Quality
by
Dorota Gumul, Eva Ivanišová, Joanna Oracz, Renata Sabat, Anna Wywrocka-Gurgul and Rafał Ziobro
Appl. Sci. 2024, 14(11), 4873; https://doi.org/10.3390/app14114873 - 4 Jun 2024
Abstract
Cherry pomace and red potato pulp were examined as a source of nutritional and health-promoting compounds in pasta products, which could gain popularity among consumers. An attempt was made to obtain such pasta with the help of low-temperature extrusion (50 °C). The purpose
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Cherry pomace and red potato pulp were examined as a source of nutritional and health-promoting compounds in pasta products, which could gain popularity among consumers. An attempt was made to obtain such pasta with the help of low-temperature extrusion (50 °C). The purpose of the study was to demonstrate which additive and in what quantity would have a more favorable effect on the nutritional, pro-health and physical quality of pasta. It was found that all pasta samples obtained with cherry pomace had a higher content of fat (10%), ash (3%), fiber (2 times) and polyphenols (22%), together with α tocopherols, than pasta with red potato pulp. Nonetheless, it had a lower water-binding capacity (20%) and higher optimum cooking time. Pasta with cherry pomace was characterized by a good taste and an attractive smell, so this additive should be recommended to obtain products with better nutritional and pro-health value and quality, especially at 30%.
Full article
(This article belongs to the Special Issue Advances in Biological Activities and Application of Plant Extracts)
Open AccessEditorial
Special Issue “Feature Review Papers in Mechanical Engineering”
by
Paolo Renna and Michele Ambrico
Appl. Sci. 2024, 14(11), 4872; https://doi.org/10.3390/app14114872 - 4 Jun 2024
Abstract
The study and advancement of crank-slide actuating mechanisms based on four-link structural groups are promising for the development of new designs of crank presses and other stamping and forging machines [...]
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(This article belongs to the Special Issue Feature Review Papers in Mechanical Engineering)
Open AccessArticle
Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks
by
Anna Jakubczyk-Gałczyńska, Agata Siemaszko and Maryna Poltavets
Appl. Sci. 2024, 14(11), 4871; https://doi.org/10.3390/app14114871 - 4 Jun 2024
Abstract
The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and financial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks
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The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and financial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks and heuristic techniques to optimize decision-making processes in construction scheduling. As an innovative approach in order to enhance construction management, the functioning of biological, molecular, and physical objects and nervous systems is considered, applying bionic features to mimic their efficiency and precision, thereby optimizing construction processes and improving coordination and decision-making. Bayesian Networks are used for probabilistic analysis, and heuristic methods guide quick decision-making. The results demonstrate the effectiveness of Bayesian Networks and heuristic methods in data analysis and decision-making in construction engineering. The developed algorithm can be successfully applied to both erecting and planning construction projects.
Full article
(This article belongs to the Section Civil Engineering)
Open AccessArticle
Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media
by
Hsin-Cheng Wu, Wu-Der Jeng, Long-Sheng Chen and Cheng-Chin Ho
Appl. Sci. 2024, 14(11), 4870; https://doi.org/10.3390/app14114870 - 4 Jun 2024
Abstract
In the transition from television to mobile devices, short videos have emerged as the primary content format, possessing tremendous potential in various fields such as marketing, promotion, education, advertising, and so on. However, from the available literature, there is a lack of studies
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In the transition from television to mobile devices, short videos have emerged as the primary content format, possessing tremendous potential in various fields such as marketing, promotion, education, advertising, and so on. However, from the available literature, there is a lack of studies investigating the elements necessary for the success of short videos, specifically regarding what factors need to be considered during production to increase viewership. Therefore, this study proposed the NLP-QFD model, integrating Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Quality Function Deployment (QFD) methods. Real short videos from mainstream Western media (CNN) and regional media (Middle East Eye) will be employed as case studies. In addition to analyzing the content of short videos and audiences’ reviews, we will utilize the NLP-QFD model to identify the key success factors (KSFs) of short videos, providing guidance for future short video creators, especially for small-scale businesses, to produce successful short videos and expand their influence through social media. The results indicate that the success factors for short videos include the movie title, promotion, reviews, and social media. For large enterprises, endorsements by famous individuals are crucial, while music and shooting are key elements for the success of short videos for small businesses.
Full article
(This article belongs to the Special Issue Knowledge and Data Engineering)
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Open AccessEditorial
Emerging Industry for Promoting Human Performance and Health: Opportunities and Challenges
by
Peng-Yeng Yin
Appl. Sci. 2024, 14(11), 4869; https://doi.org/10.3390/app14114869 - 4 Jun 2024
Abstract
In the 21st century, with a highly developed economy and a diverse cultural society, it is not uncommon to see people suffer spiritual stress and physical pain in their lives. It is an interwound negative cycle where the more pressure or pain people
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In the 21st century, with a highly developed economy and a diverse cultural society, it is not uncommon to see people suffer spiritual stress and physical pain in their lives. It is an interwound negative cycle where the more pressure or pain people feel, the greater the influence on their human performance and health, which again results in more stress and more physical pain. The high pressure entailed by overwhelming business competitions and the rapid-changing situations of global trading have a long-lasting impact on human performance and health. Clinical medical treatment usually eradicates the symptoms in the short term but shows no significant improvement in [...]
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Blockchain-Based Quality Assurance System for Academic Programs
by
Mohammad Alkhatib, Talal Albalawi and Fahman Saeed
Appl. Sci. 2024, 14(11), 4868; https://doi.org/10.3390/app14114868 - 4 Jun 2024
Abstract
Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe
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Nowadays, technology is increasingly being adopted in different kinds of businesses to process, store, and share sensitive information in digital environments that include enormous numbers of users. However, this has also increased the likelihood of cyberattacks and misuse of information, potentially causing severe damage. One promising technology, which can provide the required security services with an improved level of efficiency, is blockchain. This research explores the use of Ethereum blockchain and smart contracts to create a secure and efficient quality assurance system (QAS) for academic programs. By utilizing blockchain and smart contracts, the proposed approach improves the integrity and reliability of sensitive information processed by the QAS, promotes transparency and governance, and reduces the time and effort required for quality operations. The current approach uses an additional access control layer to further enhance user privacy. Smart contracts automate various quality transactions and saves time and resources, and hence increases the efficiency of the QAS. The interplanetary file system (IPFS) is used to address the challenge of size limitations in blockchain. Additionally, this research investigates the use of various cryptographic schemes to provide robust security services at the application layer. The experimental results showed that the use of a hybrid cryptosystem relying on an Elliptic curve digital signature and AES encryption (AES_ECCDSA) outperforms other counterparts’ cryptosystems using an RSA digital signature and AES encryption (AES_RSADSA) and Elliptic Curve Integrated Encryption Scheme (ECIES) in terms of speed. The performance results showed that AES_ECCDSA consumes 188 ms to perform the required cryptographic operations for a standard-quality document with a size of 8088 KB, compared to the 231 ms and 739 ms consumed by the AES_RSADSA and ECIES schemes, respectively. This study presents a prototype implementation of the blockchain-based QAS, which outlines the processing model and system requirements for key QAS processes. It has been found that the cost and time required for blockchain operations vary depending on the size of the input data—a larger data size requires more time and costs more to process. The results of the current study showed that the time delay for blockchain transactions ranges from 15 to 120 s, while the cost ranges from USD 50 to USD 400. This research provides evidence that blockchain and smart contract technologies have the potential to create a secure, efficient, and trustworthy QAS environment for academic programs.
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(This article belongs to the Section Computing and Artificial Intelligence)
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Open AccessArticle
Machine Learning in Assessing Canine Bone Fracture Risk: A Retrospective and Predictive Approach
by
Ernest Kostenko, Jakov Šengaut and Algirdas Maknickas
Appl. Sci. 2024, 14(11), 4867; https://doi.org/10.3390/app14114867 - 4 Jun 2024
Abstract
In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap
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In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures.
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(This article belongs to the Section Computing and Artificial Intelligence)
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