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
Compact and Highly Isolated Continuous Scanning Dual-Polarized Holographic Antenna Using a Pillbox Feeding Structure
Appl. Sci. 2024, 14(9), 3644; https://doi.org/10.3390/app14093644 (registering DOI) - 25 Apr 2024
Abstract
In this paper, we propose a novel approach to realize a compact and highly isolated dual-polarized holographic antenna using a pillbox feeding structure. The proposed antenna feeds dual orthogonal surface waves with low distortion phase distribution and high isolation through a compact three-layer
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In this paper, we propose a novel approach to realize a compact and highly isolated dual-polarized holographic antenna using a pillbox feeding structure. The proposed antenna feeds dual orthogonal surface waves with low distortion phase distribution and high isolation through a compact three-layer pillbox feeding structure. This antenna also consists of a shared aperture dual-polarized hologram pattern calculated to radiate the objective wave in the desired direction without increasing the antenna size. As a result, the proposed holographic antennas (HA) have a compact size and support forward-to-backward continuous scanning with minimal gain degradation. The simulated and measured results are in good agreement, validating the efficiency of the proposed antenna design, which has the ability to scan the beam direction from +18° to −25°, passing through the broadside within the frequency range of 21–27 GHz. Finally, the proposed antenna has a broadside gain of 18.5 dBi in each polarization and a gain variation of less than 2 dB within the operating bandwidth.
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(This article belongs to the Special Issue Technology and Application of Microwave Communication and Antenna Design)
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Land-Use Transitions Impact the Ecosystem Services Value in a Coastal Region by Coupling the Geo-Informatic Tupu and Benefit-Transfer Method: The Case of Ningde City, China
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Qingxia Peng, Lingzhi Shen, Wenxiong Lin, Shuisheng Fan and Kai Su
Appl. Sci. 2024, 14(9), 3643; https://doi.org/10.3390/app14093643 (registering DOI) - 25 Apr 2024
Abstract
Exploring the mechanisms and processes of land-use transitions (LUTs) and their impact on ecosystem services can effectively elucidate the intricate interactions between human and natural systems, which is pivotal for advancing the sustainable development of regional economies and enhancing ecological environments. However, the
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Exploring the mechanisms and processes of land-use transitions (LUTs) and their impact on ecosystem services can effectively elucidate the intricate interactions between human and natural systems, which is pivotal for advancing the sustainable development of regional economies and enhancing ecological environments. However, the existing literature lacks comprehensive analysis regarding the spatial and temporal evolution of LUTs, with insufficient integration of the “spatial pattern” and “time process”. Moreover, traditional assessments of the ecosystem services value (ESV) often overlook their negative costs. To address these gaps, this study first utilized the Google Earth Engine (GEE) cloud platform and employed the random forest algorithm to conduct supervised classification on Landsat remote-sensing images from the years 2000, 2010, and 2020 within the research area, thereby obtaining land-use data for three distinct periods. And then, we investigated the geographic features of LUTs and their ecological effects in the Ningde City of China from 2000 to 2020. The geo-informatic Tupu model and a newly revised method of benefit transfer were primarily employed for this purpose. The findings indicate the following: (1) Over the study period, the land-use structure of Ningde City predominantly comprised cultivated land and forest land, with continuous decreases in both types and a concurrent increase in built-up land. (2) Significant disparities exist in the spatial distribution of Tupu units, notably with “forest land → cultivated land” and “cultivated land → built-up land” as crucial units influencing ESV changes. (3) The ESV in Ningde City decreased from CNY 1105.54 × 108 to CNY 1020.47 × 108 over 2000–2020, while the ecosystem dis-services value exhibited an opposing trend, rising from CNY 12.68 × 108 to CNY 20.39 × 108. (4) The net ESV in Ningde City showed a decline over the same period, indicating a certain vulnerability in the city’s ecological system structure. This study aims to enhance our understanding of the influence of land-use patterns on ESV, offering valuable insights for regional ecological–environment management and land-use policy formulation, thereby fostering sustainable development in ecological, environmental, and socio-economic dimensions. Furthermore, the results serve as a reference for evaluating net ecosystem services value in other countries/regions.
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(This article belongs to the Special Issue Ecosystems and Landscape Ecology)
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A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
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Rui Liu, Jingxing Zhu, Niangang Jiao, Yao Chen and Hongjian You
Appl. Sci. 2024, 14(9), 3642; https://doi.org/10.3390/app14093642 (registering DOI) - 25 Apr 2024
Abstract
In the mosaic creation of multiple high-resolution synthetic aperture radar (SAR) images, achieving an optimal seamline in overlapping areas is crucial for seamless and visually satisfactory results. Many existing seamline generation methods are designed primarily for optical remote sensing images, but due to
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In the mosaic creation of multiple high-resolution synthetic aperture radar (SAR) images, achieving an optimal seamline in overlapping areas is crucial for seamless and visually satisfactory results. Many existing seamline generation methods are designed primarily for optical remote sensing images, but due to the differing characteristics of SAR images and optical images, applying these methods directly to SAR images poses challenges in finding the optimal seamline. In response, this paper proposes a segmentation-based optimal seamline generation (SOSG) method for SAR image mosaics. The SOSG method involves a multi-step process. First, SAR image joint segmentation is performed within the overlapping areas. Subsequently, homogeneous areas are identified based on the segmentation results. Following this, a pixel cost matrix is constructed, incorporating homogeneous areas and intensity differences. Finally, the minimum path cost from the starting pixel to the end pixel is computed using the Dijkstra algorithm to determine the optimal path. To assess the feasibility and effectiveness of the proposed method, experiments are conducted using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets. The experimental results demonstrate that the proposed method yields seamless mosaic images when compared to other methods, while delivering satisfactory outcomes. This indicates the potential of the proposed method in addressing the unique challenges posed by SAR images and enhancing the quality of SAR image mosaics.
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(This article belongs to the Collection Space Applications)
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Influence of Soy Protein Hydrolysates on Thermo-Mechanical Properties of Gluten-Free Flour and Muffin Quality
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Mihaela Brumă (Călin), Iuliana Banu, Ina Vasilean, Leontina Grigore-Gurgu, Loredana Dumitrașcu and Iuliana Aprodu
Appl. Sci. 2024, 14(9), 3640; https://doi.org/10.3390/app14093640 (registering DOI) - 25 Apr 2024
Abstract
The influence of protease-assisted hydrolysis on the impact exerted by the soy protein isolate on the thermo-mechanical behavior and baking performance of the gluten-free composite flour, consisting of a mixture of rice and quinoa flours, was investigated. The mPAGE analysis revealed that soluble
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The influence of protease-assisted hydrolysis on the impact exerted by the soy protein isolate on the thermo-mechanical behavior and baking performance of the gluten-free composite flour, consisting of a mixture of rice and quinoa flours, was investigated. The mPAGE analysis revealed that soluble fractions of the hydrolysates, obtained with bromelain, Neutrase or trypsin, concentrated the peptides with a molecular weight lower than 20 kDa, whereas the insoluble ones retained higher molecular weight fragments. The influence of the separate and cumulative addition of the soluble and insoluble soy peptide fractions on the thermo-mechanical properties of dough was tested by means of a Mixolab device. Regardless of the enzyme used for hydrolysis, the addition of the soluble peptide fraction to the gluten-free composite flour resulted in delayed starch gelatinization, whereas the insoluble one caused a considerable increase in the dough consistency. The most important improvements in the dough behavior were observed when supplementing the gluten-free flour with 10% soy protein hydrolysates obtained with bromelain and trypsin. The gluten-free muffins enriched in soy protein hydrolysate exhibited important differences in terms of moisture, height and specific volume, compared to the control. Moreover, the ABTS- and DPPH-based methods indicated that protein hydrolysate addition caused a significant improvement in the antioxidant activity (by at least 38% and 23%, respectively) compared to the control. In conclusion, soy protein hydrolysate might be successfully used for increasing both the protein content and the antioxidant activity of the muffin samples.
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(This article belongs to the Special Issue Trends in Grain Processing for Food Industry)
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Finite-Time Attitude Control of Quadrotor Unmanned Aerial Vehicle with Disturbance and Actuator Saturation
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Zheng Zhang, Xingwei Li and Lilian Zhang
Appl. Sci. 2024, 14(9), 3639; https://doi.org/10.3390/app14093639 (registering DOI) - 25 Apr 2024
Abstract
This paper introduces a nonlinear dynamic inversion control algorithm designed to address unknown disturbances and actuator saturation issues in unmanned aerial vehicle (UAV) attitude control. The algorithm is based on a combination of finite-time disturbance observer and anti-saturation auxiliary system, which ensures the
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This paper introduces a nonlinear dynamic inversion control algorithm designed to address unknown disturbances and actuator saturation issues in unmanned aerial vehicle (UAV) attitude control. The algorithm is based on a combination of finite-time disturbance observer and anti-saturation auxiliary system, which ensures the rapid convergence of attitude tracking error. Firstly, based on the Newton–Euler equations, this paper establishes a model of the attitude system for quadrotor UAVs, and this paper eliminates the small-angle flight assumption. Secondly, considering the actuator saturation problem, an anti-saturation auxiliary control system is designed to shorten the time when the control volume is in the saturation interval and achieve finite-time convergence of the attitude error. And then, to improve the robustness of the controller, this paper proposes a disturbance observer based on the finite-time stability theory, which achieves a continuous smooth output of the observation results by introducing a hyperbolic tangent function in the observer, so that the observation error can be converged to zero in a finite time. Finally, it is demonstrated by Simulink simulation that the attitude error and the observation error converge quickly to zero.
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(This article belongs to the Section Aerospace Science and Engineering)
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Challenges and Opportunities for Pilot Scaling-Up Extraction of Olive Oil Assisted by Pulsed Electric Fields: Process, Product, and Economic Evaluation
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Sara Dias, Enrique Pino-Hernández, Diogo Gonçalves, Duarte Rego, Luís Redondo and Marco Alves
Appl. Sci. 2024, 14(9), 3638; https://doi.org/10.3390/app14093638 (registering DOI) - 25 Apr 2024
Abstract
This study aimed to investigate the impact of Pulsed Electric Fields (PEF) technology in the extraction of olive oil on a pilot scale, using the “Galega Vulgar” olive variety as raw material. The extraction assisted by PEF had a malaxation time of 30
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This study aimed to investigate the impact of Pulsed Electric Fields (PEF) technology in the extraction of olive oil on a pilot scale, using the “Galega Vulgar” olive variety as raw material. The extraction assisted by PEF had a malaxation time of 30 min and was compared with the traditional process of 45 min of malaxation. The main quality parameters of olive oil and the PEF’s cost-benefit assessment were performed. The incorporation of PEF in olive oil production reduced the malaxation stage by 33% without compromising the yield or extra-virgin classification. This efficiency leads to a potential 12.3% increase in annual olive oil production, with a 12.3% and 36.8% rise in revenue and gross profit, respectively. For small-scale production, the considerable upfront investment required for PEF equipment may be a challenge in terms of return on investment. In this scenario, opting for a renting scheme is the best economic solution, especially given the seasonal nature of olive oil production. In medium- to large-scale production, the investment in PEF is a sound investment since it is possible to achieve, with an equipment cost of EUR 450,000 and a production output of 5 tons per hour, an annual ROI of 20%.
Full article
(This article belongs to the Special Issue Applications of Pulsed Electric Field (PEF) Interactions with Biological Cells)
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Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
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Zhenyu He, Yulin Sun and Zhen Zhang
Appl. Sci. 2024, 14(9), 3637; https://doi.org/10.3390/app14093637 (registering DOI) - 25 Apr 2024
Abstract
In recent years, the continuous progress of wireless communication and sensor technology has enabled sensors to be better integrated into mobile devices. Therefore, sensor-based Human Activity Recognition (HAR) has attracted widespread attention among researchers, especially in the fields of wearable technology and ubiquitous
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In recent years, the continuous progress of wireless communication and sensor technology has enabled sensors to be better integrated into mobile devices. Therefore, sensor-based Human Activity Recognition (HAR) has attracted widespread attention among researchers, especially in the fields of wearable technology and ubiquitous computing. In these applications, mobile devices’ built-in accelerometers and gyroscopes have been typically used for human activity recognition. However, devices such as smartphones were placed in users’ pockets and not fixed to their bodies, and the resulting changes in the orientation of the sensors due to users’ habits or external forces can lead to a decrease in the accuracy of activity recognition. Unfortunately, there is currently a lack of publicly available datasets specifically designed to address the issue of device angle change. The contributions of this study are as follows. First, we constructed a dataset with eight different sensor placement angles using accelerometers and gyroscopes as a prerequisite for the subsequent research. Second, we introduced the Madgwick algorithm to extract quaternion mode features and alleviate the impact of angle changes on recognition performance by fusing raw accelerometer data and quaternion mode features. The resulting study provides a comprehensive analysis. On the one hand, we fine-tuned ResNet and tested its stability on our dataset, achieving a recognition accuracy of 97.13%. We included two independent experiments, one for user-related scenarios and the other for user-independent scenarios. In addition, we validated our research results on two publicly available datasets, demonstrating that our method has good generalization performance.
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(This article belongs to the Section Computing and Artificial Intelligence)
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Optimal Timing of Carrot Crop Monitoring and Yield Assessment Using Sentinel-2 Images: A Machine-Learning Approach
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Rangaswamy Madugundu, Khalid A. Al-Gaadi, ElKamil Tola, Mohamed K. Edrris, Haroon F. Edrees and Ahmed A. Alameen
Appl. Sci. 2024, 14(9), 3636; https://doi.org/10.3390/app14093636 (registering DOI) - 25 Apr 2024
Abstract
Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and
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Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production.
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(This article belongs to the Special Issue Geospatial Technology: Modern Applications and Their Impact)
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Digitizing Historical Aerial Images: Evaluation of the Effects of Scanning Quality on Aerial Triangulation and Dense Image Matching
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Adam Kostrzewa, Elisa Mariarosaria Farella, Luca Morelli, Wojciech Ostrowski, Fabio Remondino and Krzysztof Bakuła
Appl. Sci. 2024, 14(9), 3635; https://doi.org/10.3390/app14093635 (registering DOI) - 25 Apr 2024
Abstract
In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block
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In the last decade, many aerial photographic archives have started to be digitized for multiple purposes, including digital preservation and geoprocessing. This paper analyzes the effects of professional photogrammetric versus consumer-grade scanners on the processing of analog historical aerial photographs. An image block over Warsaw is considered, featuring 38 photographs acquired in 1986 (Wild RC10, Normal Aviogon II lens, 23 × 23 cm format) with a ground sampling distance (GSD) of 4 cm. Aerial triangulation (AT) and dense image matching (DIM) procedures are considered, analyzing how scanning modalities are important in the massive digitization of analog images for georeferencing and 3D product generation. The achieved results show how consumer-grade scanners, unlike more expensive photogrammetric scanners, do not possess adequate recording quality to ensure high accuracy and geometric precision for geoprocessing purposes. However, consumer-grade scanners can be used for time and cost-efficient applications where a partial loss of data quality is not critical.
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(This article belongs to the Special Issue Geo-Processing of Historical Aerial Images)
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Monitoring Horizontal Displacements with Low-Cost GNSS Systems Using Relative Positioning: Performance Analysis
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Burak Akpınar and Seda Özarpacı
Appl. Sci. 2024, 14(9), 3634; https://doi.org/10.3390/app14093634 (registering DOI) - 25 Apr 2024
Abstract
Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning
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Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system’s capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system’s performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks.
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(This article belongs to the Special Issue Identification and Measurement of Displacements and Deformations of Engineering Structures: 2nd Edition)
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Non-Conscious Affective Processing in Asset Managers during Financial Decisions: A Neurobiological Perspective
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Peter Walla and Maximilian Patschka
Appl. Sci. 2024, 14(9), 3633; https://doi.org/10.3390/app14093633 (registering DOI) - 25 Apr 2024
Abstract
In the world of finance, considerable attention is given to improving machine learning techniques to predict the future of stock markets. However, for obvious reasons, this turns out to be an unsolvable mission, most likely because the real world is not driven by
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In the world of finance, considerable attention is given to improving machine learning techniques to predict the future of stock markets. However, for obvious reasons, this turns out to be an unsolvable mission, most likely because the real world is not driven by algorithms but by human beings. In response to this, the present study has its focus on raw affective responses in actual asset managers during their decision making regarding controlled financial scenarios. Nineteen asset managers were invited and asked to make sell/buy decisions related to visual presentations of three different price developments of different assets. The three scenarios were “crash”, “stable” and “gain”. Parallel to their decision making, startle reflex modulation (SRM) was used to measure non-conscious affective responses without demanding any respective explicit responses (no conscious language processing involved). Interestingly, two further factors were introduced. First, all participants had to make their decisions once while being informed that 0% prior investments (low exposure) have been made into the presented assets, and once being informed that a large investment consisting of 25% of ones’ overall portfolio has been made prior to making the decision (high exposure). Second, the factor experience was included dividing all participants into two groups, one with low experience and the other with high experience. First, across both these extra factors, it was found that “crash” scenarios resulted in the most negative affective responses. The most positive affective responses were found for “gain” scenarios, while the “stable” condition was in between. Interestingly, the factor of prior investment (i.e., exposure) had an effect. Non-conscious affective responses during decision making related to the “stable” condition varied as a function of “exposure”. In the low exposure condition, affective responses to decision making during the “stable” scenario were most negative, even more negative than in “crash” scenarios. The factor experience also had an effect, but due to the small sample size, no significant interaction occurred. However, t-tests revealed the same significant effects in the experienced group as found in the 0% prior investment condition. To our knowledge, this is the first empirical investigation measuring non-conscious affective responses during decision making in the context of asset management. Thus, this study might form an interesting basis for new strategies to explore non-conscious human brain functions instead of inventing new algorithms to make asset management more successful.
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(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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Improvement of Fresnel Diffraction Convolution Algorithm
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Cong Ge, Qinghe Song, Weinan Caiyang, Jinbin Gui, Junchang Li, Xiaofan Qian, Qian Li and Haining Dang
Appl. Sci. 2024, 14(9), 3632; https://doi.org/10.3390/app14093632 (registering DOI) - 25 Apr 2024
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With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance , which
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With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance , which reduces the accuracy of solving the phase. In this paper, the Fresnel diffraction integration algorithm is improved by using the low-pass Tukey window to obtain more accurate reconstructed phases. The improved algorithm is called the D-FFT (Tukey) algorithm. The D-FFT (Tukey) algorithm adjusts the degree of edge smoothing of the Tukey window, using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) to remove the ringing effect and obtain a more accurate reconstructed phase. In a simulation of USAF1951, the longitudinal resolution of the reconstructed phase obtained by D-FFT (Tukey) reached 1.5 , which was lower than the 3 obtained by the T-FFT algorithm. The results of Fresnel holography experiments on lung cancer cell slices also demonstrated that the phase quality obtained by the D-FFT (Tukey) algorithm was superior to that of the T-FFT algorithm. D-FFT (Tukey) algorithm has potential applications in phase correction, structured illumination digital holographic microscopy, and microscopic digital holography.
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Exploring the Effect of Moisture on CO2 Diffusion and Particle Cementation in Carbonated Steel Slag
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Shenqiu Lin, Ping Chen, Weiheng Xiang, Cheng Hu, Fangbin Li, Jun Liu and Yu Ding
Appl. Sci. 2024, 14(9), 3631; https://doi.org/10.3390/app14093631 (registering DOI) - 25 Apr 2024
Abstract
The study of the mechanisms affecting the preparation parameters of carbonated steel slag is of great significance for the development of carbon sequestration materials. In order to elucidate the mechanism of the influence of moisture on CO2 diffusion and particle cementation in
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The study of the mechanisms affecting the preparation parameters of carbonated steel slag is of great significance for the development of carbon sequestration materials. In order to elucidate the mechanism of the influence of moisture on CO2 diffusion and particle cementation in steel slag, the effects of different water–solid ratios and water contents on the mechanical properties, carbonation products, and pore structure of steel slag after carbonation were investigated. The results show that increasing the water–solid ratio of steel slag can control the larger initial porosity and improve the carbon sequestration capacity of steel slag, but it will reduce the mechanical properties. The carbonation process relies on pores for CO2 diffusion and also requires a certain level of moisture for Ca2+ dissolution and diffusion. Increasing the water content enhances particle cementation and carbonation capacity in steel slag specimens; however, excessive water hinders CO2 diffusion. Reducing the water content can increase the carbonation depth but may compromise gelling and carbon sequestration ability. Therefore, achieving a balance is crucial in controlling the water content. The compressive strength of the steel slag with suitable moisture and initial porosity can reach 118.7 MPa, and 217.2 kg CO2 eq./t steel slag can be sequestered.
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(This article belongs to the Special Issue Development, Characterization, Application and Recycling of Novel Construction Materials)
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Physicochemical, Antioxidant, Antimicrobial, and Sensory Characteristics of Selected Kinds of Edible Oils
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Eva Ivanišová, Veronika Juricová, Július Árvay, Miroslava Kačániová, Matej Čech, Zbigniew Kobus, Monika Krzywicka, Wojciech Cichocki and Katarzyna Kozłowicz
Appl. Sci. 2024, 14(9), 3630; https://doi.org/10.3390/app14093630 (registering DOI) - 25 Apr 2024
Abstract
The aim of this study was to determine the peroxide values, acid numbers, oxidative stability (Rancimat method), antioxidant activity (DPPH method), antimicrobial activity (disc diffusion method), sensory properties (9-point hedonic scale), and fatty acid profiles (FAME) of five edible oils purchased from local
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The aim of this study was to determine the peroxide values, acid numbers, oxidative stability (Rancimat method), antioxidant activity (DPPH method), antimicrobial activity (disc diffusion method), sensory properties (9-point hedonic scale), and fatty acid profiles (FAME) of five edible oils purchased from local Slovakian producers—grape seed oil, flax seed oil, walnut oil, poppy seed oil, and milk thistle seed oil. The peroxide value ranged from 2.27 (milk thistle oil) to 8.51 (flax seed oil) mmol O2/kg. All these values were in accordance with regulations (upper limit of 20 mmol O2/kg). The values of the acid number ranged from 0.11 (walnut oil) to 2.49 (milk thistle oil) mg KOH/g, and were in accordance with regulations as they did not exceed the value of 4 mg KOH/g. The oxidation stability was the lowest in flax seed oil (0.18 h) and the highest in grape seed oil (2.05 h). In milk thistle oil, the highest amounts of oleic and behenic acids, in flax seed oil, the highest amount of α-linolenic acid, and in grape seed oil, the highest amount of linolic acid were determined. Antioxidant activity was the strongest in the sample of grape seed oil—65.53 mg TEAC/L (Trolox equivalent antioxidant capacity). Samples of flax seed oil showed the strongest inhibition of Candida albicans CCM 8186 (4.58 mm) and Bacillus subtilis CCM 2010 (0.31 mm). Poppy seed oil was determined to be the most inhibiting towards Klebsiella pneumoniae CCM 2318 (3.68 mm). Milk thistle oil showed the strongest inhibition of Clostridium perfringens CCM 4435 (6.31 mm). Grape seed oil was the most inhibitory towards Staphylococcus aureus subs. aureus CCM 2461 (5.32 mm). Walnut oil showed the strongest activity towards Yersinia enterocolitica CCM 5671 (6.33 mm). The sensory analysis resulted in the samples of walnut and grape seed oil being awarded the highest scores for smell, taste, and overall acceptability. The tested edible oils are rich in biologically active compounds with antioxidant and antimicrobial activities. Their consumption can have a positive effect on the functioning of the human body and its health. Proper storage conditions are, however, necessary because of the susceptibility of these oils to oxidation.
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(This article belongs to the Section Food Science and Technology)
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Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer
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Chenghao Si, Hui Luo, Yuelin Han and Zhiwei Ma
Appl. Sci. 2024, 14(9), 3629; https://doi.org/10.3390/app14093629 (registering DOI) - 25 Apr 2024
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With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep
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With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.
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Open AccessArticle
Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
by
Jinlong Wang, Zhezhuang Xu, Mingxing He, Liang Xue and Hongjie Xu
Appl. Sci. 2024, 14(9), 3628; https://doi.org/10.3390/app14093628 (registering DOI) - 25 Apr 2024
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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this
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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.
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Open AccessArticle
The Effect of Situational Variables on Women’s Rink Hockey Match Outcomes
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Jordi Arboix-Alió, Guillem Trabal, Dani Moreno-Galcerán, Bernat Buscà, Adrià Arboix, Vasco Vaz, Hugo Sarmento and Raúl Hileno
Appl. Sci. 2024, 14(9), 3627; https://doi.org/10.3390/app14093627 (registering DOI) - 25 Apr 2024
Abstract
The main objective of the present study was to develop a concise predictive model to determine the likelihood of winning in female rink hockey based on various situational variables. Additionally, the study aimed to assess the individual impact of each predictor on match
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The main objective of the present study was to develop a concise predictive model to determine the likelihood of winning in female rink hockey based on various situational variables. Additionally, the study aimed to assess the individual impact of each predictor on match outcomes. The analysis encompassed a dataset of 840 matches during five consecutive seasons (from 2018–2019 to 2022–2023) in the Spanish first division (OkLiga). Employing the comprehensive method of all possible regressions, the most effective predictive logistic model for match outcomes was identified. This entire model featured five categorical predictor variables (match location, team level, opponent level, scoring first, and match status at halftime) and one binary outcome variable (match outcome). Subsequently, the final model, which exhibited a sensitivity and specificity surpassing 80% for a cut-off point of 0.439, emerged. This model was applied to predict winning a match in 18 frequent situations determined from a two-step cluster analysis. Within this predictive framework, match status at halftime emerged as the most influential predictor impacting the match outcome, followed by opponent level, team level, and match location. The implications of our findings extend to rink hockey coaches and practitioners. Recognizing the significant impact of situational variables on match outcomes empowers them to customize game plans and design more specific strategies, thereby enhancing game understanding and elevating the overall performance.
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(This article belongs to the Special Issue Advances in Performance Analysis and Technology in Sports)
Open AccessArticle
Study on the Influence of Adjacent Double Deep Foundation Pit Excavation Sequence on Existing Tunnel Deformation Based on HSS Constitutive Model
by
Sijun Wang, Wenting Wang, Huan Yang, Debin Zhao and Yang Liu
Appl. Sci. 2024, 14(9), 3626; https://doi.org/10.3390/app14093626 (registering DOI) - 25 Apr 2024
Abstract
With the increase in the number of buildings along the subway, the impact of building construction on the adjacent subway tunnels has gradually come to the forefront and become an important problem to be solved in the engineering field. In particular, the excavation
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With the increase in the number of buildings along the subway, the impact of building construction on the adjacent subway tunnels has gradually come to the forefront and become an important problem to be solved in the engineering field. In particular, the excavation and unloading process of deep foundation pits will trigger an additional deformation of the subway structure, which may pose a serious threat to the safety and stability of subway tunnels. This article is based on a foundation pit project in the sub-center of Beijing, focusing on the form of a connected double foundation pit. Using the HSS constitutive model for soil materials, this study simulates the deformation response of adjacent existing subway tunnels under three excavation sequences: sequential excavation, simultaneous excavation, and the comprehensive excavation of the connected double foundation pits. The study shows that, from the point of view of the total displacement of the whole construction process, the impact of a synchronized excavation of double pits on the existing tunnel line is relatively large in the process, and the impact of sequential excavation is relatively small in the construction cycle. The result of the similarity of the excavation sequence is the similarity of the impact trend. The volume of excavated earth determines the value of displacement change for each excavation scenario in each working condition and is also responsible for the convergence of changes. The trend of total tunnel displacement is more consistent with that of vertical displacement, which is dominated by vertical displacement, with horizontal displacement having a relatively small influence. The maximum value of the total tunnel displacement occurs at the side of the tunnel near the excavation area, and the direction is inclined to the excavation area. The application of supporting structures, especially the center plate and the bottom plate, can suppress the vertical deformation of the tunnel bulge.
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(This article belongs to the Special Issue Structural Health Monitoring of Tunnel and Underground Engineering)
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Simple Ultrasonic-Based Localization System for Mobile Robots
by
Marek Sukop, Maksym Grytsiv, Rudolf Jánoš and Ján Semjon
Appl. Sci. 2024, 14(9), 3625; https://doi.org/10.3390/app14093625 (registering DOI) - 25 Apr 2024
Abstract
This paper presents the development and validation of a cost-efficient and uncomplicated real-time localization system (RTLS) for use in mobile robotics, specifically within indoor and storage environments. By harnessing ultrasonic waves to measure distances from three beacons, the system provides stable and reliable
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This paper presents the development and validation of a cost-efficient and uncomplicated real-time localization system (RTLS) for use in mobile robotics, specifically within indoor and storage environments. By harnessing ultrasonic waves to measure distances from three beacons, the system provides stable and reliable localization. This method utilizes the time-of-flight (TOF) principle, allowing for accurate distance calculations with simple microcontrollers. The system is designed to update the robot’s position at a frequency of at least 10 times per second, ensuring smooth navigation. Our trilateration-based approach allows for the precise determination of the robot’s position with a notable standard deviation accuracy of up to 15 mm. The aim was to design a simple yet sufficiently accurate system and verify its precision through experimental measurements. The experimental results demonstrate the system’s efficacy and lay a solid foundation for advancing this technology. Furthermore, the cost for the components required to build this indoor localization system (ILS) with three beacons and one tag is remarkably low, under EUR 80.
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(This article belongs to the Special Issue Novel Methods and Technologies for Intelligent Vehicles (Volume II))
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A New Data Processing Approach for the SHPB Test Based on PSO-TWER
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Xuesong Wang, Zhenyang Xu and Lianjun Guo
Appl. Sci. 2024, 14(9), 3624; https://doi.org/10.3390/app14093624 (registering DOI) - 25 Apr 2024
Abstract
This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A
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This study addresses the challenge of accurately determining the arrival time of stress wave signals in SHPB test data processing. To eliminate human error, we introduce the time-window energy ratio method and evaluate six filters for noise reduction using box fractal dimensions. A mathematical model is established to optimize the stress equilibrium and impact process, which is solved using particle swarm optimization, resulting in the PSO-TWER method. We explore the impact of inertia weight and calculation methods on optimization outcomes, defining a stress equilibrium evaluation index. The results indicate that time-window length significantly affects arrival-time outputs, and the dynamic inertia weight factor enhances optimization convergence. The method accurately determines arrival times and effectively screens test data, providing a robust approach for SHPB test data processing.
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(This article belongs to the Section Materials Science and Engineering)
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