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10 pages, 3495 KiB  
Technical Note
Machine Learning for Predicting Neutron Effective Dose
by Ali A. A. Alghamdi
Appl. Sci. 2024, 14(13), 5740; https://doi.org/10.3390/app14135740 (registering DOI) - 1 Jul 2024
Abstract
The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations [...] Read more.
The calculation of effective doses is crucial in many medical and radiation fields in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) from different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and it is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission on Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R2 metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry. Full article
(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)
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16 pages, 6965 KiB  
Article
Spatiotemporal Variation and Predictors of the Purpleback Flying Squid (Sthenoteuthis oualaniensis) Distribution Surrounding the Xisha and Zhongsha Islands during a Fishing Moratorium
by Liangming Wang, Changping Yang, Binbin Shan, Yan Liu, Jianwei Zou, Dianrong Sun and Tao Guo
Fishes 2024, 9(7), 253; https://doi.org/10.3390/fishes9070253 (registering DOI) - 1 Jul 2024
Abstract
As an economic species widely distributed in the South China Sea (SCS), the purpleback flying squid (Sthenoteuthis oualaniensis) still has a large potential for exploitation, and the variations in its use as a resource are highly correlated with environmental and other [...] Read more.
As an economic species widely distributed in the South China Sea (SCS), the purpleback flying squid (Sthenoteuthis oualaniensis) still has a large potential for exploitation, and the variations in its use as a resource are highly correlated with environmental and other factors. In this study, using a generalized additive model (GAM) and gradient forest analysis (GFA), in conjunction with environmental factors, the distribution of purpleback flying squid surrounding the Xisha and Zhongsha islands during the fishing moratorium period was investigated. The results indicated that catch per unit effort (CPUE) had a gradual increase from May to July 2023 in the primary fishing area surrounded the Xisha Islands during May to June, then moved southward towards 13–15° N after July. CPUE is used as an important indicator to reflect the abundance of the fishery, while the GFA results show that CPUE has a better fit than catch in this study. Therefore, the subsequent analysis focused on CPUE. Longitude and sea surface temperature (SST) were of relative higher importance, followed by sea surface salinity (SSS), latitude, chlorophyll a concentration (Chla), sea surface height (SSH), and mixed layer depth (MLD). Longitude and CPUE had a significant, positive correlation. The CPUE gradually increased with latitude within 14–16° N. The CPUE increased slowly as SST increased from 29.5 to 30.5 °C in the primary fishing area. The Chla in this fishing zone was 0–0.2 mg/m3 and displayed a significant positive association with CPUE. Conversely, SSS, SSH, and MLD had negative correlations with CPUE. These findings will promote the sustainable utilization of purpleback flying squid in the SCS. Full article
(This article belongs to the Special Issue Assessment and Management of Fishery Resources)
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17 pages, 2543 KiB  
Article
Named Entity Recognition for Chinese Texts on Marine Coral Reef Ecosystems Based on the BERT-BiGRU-Att-CRF Model
by Danfeng Zhao, Xiaolian Chen and Yan Chen
Appl. Sci. 2024, 14(13), 5743; https://doi.org/10.3390/app14135743 (registering DOI) - 1 Jul 2024
Abstract
In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading [...] Read more.
In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields. Full article
(This article belongs to the Special Issue Environmental Monitoring and Analysis for Hydrology)
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15 pages, 2195 KiB  
Article
Increased Dissemination of Aflatoxin- and Zearalenone-Producing Aspergillus spp. and Fusarium spp. during Wet Season via Houseflies on Dairy Farms in Aguascalientes, Mexico
by Erika Janet Rangel-Muñoz, Arturo Gerardo Valdivia-Flores, Carlos Cruz-Vázquez, María Carolina de-Luna-López, Emmanuel Hernández-Valdivia, Irene Vitela-Mendoza, Leticia Medina-Esparza and Teódulo Quezada-Tristán
Toxins 2024, 16(7), 302; https://doi.org/10.3390/toxins16070302 (registering DOI) - 1 Jul 2024
Abstract
Crops contamination with aflatoxins (AFs) and zearalenone (ZEA) threaten human and animal health; these mycotoxins are produced by several species of Aspergillus and Fusarium. The objective was to evaluate under field conditions the influence of the wet season on the dissemination of [...] Read more.
Crops contamination with aflatoxins (AFs) and zearalenone (ZEA) threaten human and animal health; these mycotoxins are produced by several species of Aspergillus and Fusarium. The objective was to evaluate under field conditions the influence of the wet season on the dissemination of AF- and ZEA-producing fungi via houseflies collected from dairy farms. Ten dairy farms distributed in the semi-arid Central Mexican Plateau were selected. Flies were collected in wet and dry seasons at seven points on each farm using entomological traps. Fungi were isolated from fly carcasses via direct seeding with serial dilutions and wet chamber methods. The production of AFs and ZEA from pure isolates was quantified using indirect competitive ELISA. A total of 693 Aspergillus spp. and 1274 Fusarium spp. isolates were obtained, of which 58.6% produced AFs and 50.0% produced ZEA (491 ± 122; 2521 ± 1295 µg/kg). Houseflies and both fungal genera were invariably present, but compared to the dry season, there was a higher abundance of flies as well as AF- and ZEA-producing fungi in the wet season (p < 0.001; 45.3/231 flies/trap; 8.6/29.6% contaminated flies). These results suggest that rainy-weather conditions on dairy farms increase the spread of AF- and ZEA-producing Aspergillus spp. and Fusarium spp. through houseflies and the incorporation of their mycotoxins into the food chain. Full article
(This article belongs to the Special Issue Mycotoxins in Farm Animals: Health Disorders and Preventive Strategies)
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11 pages, 1905 KiB  
Article
Oxygen Species Involved in Complete Oxidation of CH4 by SrFeO3-δ in Chemical Looping Reforming of Methane
by Jianan Hao, Liuqing Yang and Junshe Zhang
Materials 2024, 17(13), 3212; https://doi.org/10.3390/ma17133212 (registering DOI) - 1 Jul 2024
Abstract
Compared with conventional methane reforming technologies, chemical looping reforming (CLR) has the advantages of self-elimination of coke, a suitable syngas ratio for certain down-stream processes, and a pure H2 or CO stream. In the reduction step of CLR, methane combustion has to [...] Read more.
Compared with conventional methane reforming technologies, chemical looping reforming (CLR) has the advantages of self-elimination of coke, a suitable syngas ratio for certain down-stream processes, and a pure H2 or CO stream. In the reduction step of CLR, methane combustion has to be inhibited, which could be achieved by designing appropriate oxygen carriers and/or optimizing the operating conditions. To gain a further understanding of the combustion reaction, methane oxidation by perovskite (SrFeO3-δ) at 900 °C and 1 atm in a pulse mode was investigated in this work. The oxygen non-stoichiometry of SrFeO3-δ prepared by a Pechini-type polymerizable complex method is 0.14 at ambient conditions, and it increases to 0.25 and subsequently to 0.5 when heating from 100 to 900 °C in argon that contains 2 ppmv of molecular oxygen. The activation energies of the first and second transitions are 294 and 177 kJ/mol, respectively. The presence of 0.99 vol.% hydrogen in argon significantly reduces the amount CO2 produced. At a pulse interval of 10 min, the amount of CO2 produced in the absence of hydrogen is one order of magnitude greater than that in the presence of hydrogen. In the former case, the amount of CO2 produced dramatically decreases first and then gradually approaches a constant, and the oxygen species involved in methane combustion can be partially replenished by extending the pulse interval, e.g., 82.5% of this type of oxygen species is replenished when the pulse interval is extended to 60 min. The restored species predominantly originate from those that reside in the surface layer or even in the bulk. Full article
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8 pages, 595 KiB  
Article
Phytotoxicity of Two Bauhinia Species on Four Triticum aestivum Varieties in Laboratory Bioassay
by Neeraj Yadav, Vinod Prasad Khanduri, Bhupendra Singh, Deepa Rawat and Manoj Kumar Riyal
Int. J. Plant Biol. 2024, 15(3), 599-606; https://doi.org/10.3390/ijpb15030045 (registering DOI) - 1 Jul 2024
Abstract
Tree–crop interaction studies help to determine the effects of trees on the production and yield of agricultural crops and could help indecisions on suitable crops and tree combinations to increase the overall production from agroforestry systems. Different varieties of agricultural crops might show [...] Read more.
Tree–crop interaction studies help to determine the effects of trees on the production and yield of agricultural crops and could help indecisions on suitable crops and tree combinations to increase the overall production from agroforestry systems. Different varieties of agricultural crops might show different responses against the phytotoxic effects of Bauhinia species. This study was conducted to observe the phytotoxicity of two Bauhinia spp., i.e., Bauhinia retusa and Bauhinia variegata, on some Triticum aestivum varieties, i.e., VL-892, VI-829, VL-616, UP-2572, and UP-1109.The leaves and bark of these two species were harvested from the natural population for these experiments. On average, germination and radicle and plumule length of wheat varieties were significantly (p > 0.05) reduced by the leaf and bark extracts of both Bauhinia species. The effect of leaf and bark extracts of both Bauhinia species on seed germination percent of different wheat varieties revealed that the bark and leaf extracts showed maximum toxicity for germination percentage, and minimum influence was observed in radicle and plumule length. However, bark extracts were more toxic as compared to leaf extracts. Under leaf and bark extract concentrations, the VL 829 wheat variety showed stimulatory effects in germination and radicle and plumule growth under both Bauhinia species. On average, radicle and plumule growth of the test crop was increased with an increasing concentration of leaf and bark extract of B. variegata up to 50%, and thereafter, a decrease in radicle and plumule length was recorded. The VL 829 and UP 1109 varieties showed the lowest allelopathic effects and could be grown under both Bauhinia species with minimum yield loss. Full article
(This article belongs to the Section Plant Physiology)
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13 pages, 4099 KiB  
Article
The Novel EfficientNet Architecture-Based System and Algorithm to Predict Complex Human Emotions
by Mavlonbek Khomidov and Jong-Ha Lee
Algorithms 2024, 17(7), 285; https://doi.org/10.3390/a17070285 (registering DOI) - 1 Jul 2024
Abstract
Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural [...] Read more.
Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states. Full article
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13 pages, 2418 KiB  
Article
Peak Load Shaving of Air Conditioning Loads via Rooftop Grid-Connected Photovoltaic Systems: A Case Study
by Reza Bakhshi-Jafarabadi and Seyed Mahdi Seyed Mousavi
Sustainability 2024, 16(13), 5640; https://doi.org/10.3390/su16135640 (registering DOI) - 1 Jul 2024
Abstract
Over the past few decades, grid-connected photovoltaic systems (GCPVSs) have been consistently installed due to their techno-socio-economic-environmental advantages. As an effective solution, this technology can shave air conditioning-based peak loads on summer days at noon in hot areas. This paper assesses the effect [...] Read more.
Over the past few decades, grid-connected photovoltaic systems (GCPVSs) have been consistently installed due to their techno-socio-economic-environmental advantages. As an effective solution, this technology can shave air conditioning-based peak loads on summer days at noon in hot areas. This paper assesses the effect of solely rooftop GCPVS installations on the peak load shaving of commercial buildings in arid regions, e.g., the Middle East and North Africa. To this end, the load profile of a large building with 470 kW of unshaved peak power in Mashhad, Iran (36.2972° N, 59.6067° E) is analyzed after commissioning an actual 51 kW GCPVS. The results of this experimental study, exploiting 15 min resolution data over a year, endorse an effective peak shaving of the GCPVS without employing a battery energy storage system, with 12.2–18.5% peak power shaving on a summer day at noon. The monthly GCPVS self-sufficiency is also 10.2%, on average. In accordance with the studied case’s results, this paper presents valuable insights and recommends actionable policies to regions with similar solar potential and electricity supply challenges, aiming to expedite GCPVS development. Full article
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20 pages, 7850 KiB  
Article
Study of the Structure of Zn and Na Borophosphate Glasses Using X-ray and Neutron Scattering Techniques
by Uwe Hoppe, Parker T. Freudenberger, Richard K. Brow, Jozef Bednarčik and Alex C. Hannon
Solids 2024, 5(3), 355-374; https://doi.org/10.3390/solids5030024 (registering DOI) - 1 Jul 2024
Abstract
The atomic structures of Zn and Na borophosphate glasses were studied using X-ray and neutron scattering techniques. Peaks assigned to the B−O, P−O, and O−O distances confirm that only BO4 units co-exist with the PO4 tetrahedra. The Zn−O and Na−O coordination [...] Read more.
The atomic structures of Zn and Na borophosphate glasses were studied using X-ray and neutron scattering techniques. Peaks assigned to the B−O, P−O, and O−O distances confirm that only BO4 units co-exist with the PO4 tetrahedra. The Zn−O and Na−O coordination numbers are found to be a little larger than four. The narrowest peaks of the Zn−O first-neighbor distances exist for the glasses along a line connecting the Zn(PO3)2 and BPO4 compositions (50 mol% P2O5), which is explained by networks of ZnO4, BO4, and PO4 tetrahedra with twofold coordinated oxygens. The calculated amounts of available oxygen support this interpretation. Broadened peaks occur for glasses with lower P2O5 contents, which is consistent with the presence of threefold coordinated oxygens. The two distinct P−O peak components of the Zn and Na borophosphate glasses differ in their relative abundances. This is interpreted as follows: Na+ cations coordinate oxygens in some P−O−B bridges, which is something not seen for the Zn2+ ions. Full article
(This article belongs to the Special Issue A Themed Issue in Honour of Professor Alexandra Navrotsky on the Occasion of Her 80th Birthday)
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24 pages, 3951 KiB  
Article
A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components
by Fernando Morilla, Jesús Vega, Sebastián Dormido-Canto, Amor Romero-Maestre, José de-Martín-Hernández, Yolanda Morilla, Pedro Martín-Holgado and Manuel Domínguez
Sensors 2024, 24(13), 4276; https://doi.org/10.3390/s24134276 (registering DOI) - 1 Jul 2024
Abstract
This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components [...] Read more.
This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as ‘recognition of degradation patterns in the database’ and ‘degradation prediction of new samples without any kind of irradiation’. The technique can be used under two different approaches called ‘pure data driven’ and ‘model based’. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component. Full article
(This article belongs to the Section Intelligent Sensors)
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12 pages, 1981 KiB  
Article
Comparison of a Two (32/38 Weeks) versus One (36 Weeks) Ultrasound Protocol for the Detection of Decreased Fetal Growth and Adverse Perinatal Outcome
by Mar Nieto-Tous, Blanca Novillo-Del Álamo, Alicia Martínez-Varea, Elena Satorres-Pérez and José Morales-Roselló
J. Pers. Med. 2024, 14(7), 709; https://doi.org/10.3390/jpm14070709 (registering DOI) - 1 Jul 2024
Abstract
Third-trimester ultrasound has low sensitivity to small for gestational age (SGA) and adverse perinatal outcomes (APOs). The objective of this study was to compare, in terms of cost-effectiveness, two routine third-trimester surveillance protocols for the detection of SGA and evaluate the added value [...] Read more.
Third-trimester ultrasound has low sensitivity to small for gestational age (SGA) and adverse perinatal outcomes (APOs). The objective of this study was to compare, in terms of cost-effectiveness, two routine third-trimester surveillance protocols for the detection of SGA and evaluate the added value of a Doppler study for the prediction of APO. This was a retrospective observational study of low-risk pregnancies that were followed by a two growth scans protocol (P2) at 32 and 38 weeks or by a single growth scan at 36 weeks (P1). Ultrasound scans included an estimated fetal weight (EFW) in all cases and a Doppler evaluation in most cases. A total of 1011 pregnancies were collected, 528 with the P2 protocol and 483 with the P1 protocol. While the two models presented no differences for the detection of SGA in terms of sensitivity (47.89% vs. 50% p = 0.85) or specificity (94.97 vs. 95.86% p = 0.63), routine performance of two growth scans (P2) led to a 35% cost increase. The accuracy of EFW for the detection of SGA showed a noteworthy improvement when reducing the interval to labor, and the only parameter with predictive capacity of APO was the cerebroplacental ratio at 38 weeks. In low-risk pregnancies, the higher costs of a two-scan growth surveillance protocol at the third trimester are not justified by an increase in diagnostic effectivity. Full article
(This article belongs to the Section Clinical Medicine, Cell, and Organism Physiology)
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31 pages, 14424 KiB  
Article
Enhancing Video Anomaly Detection Using a Transformer Spatiotemporal Attention Unsupervised Framework for Large Datasets
by Mohamed H. Habeb, May Salama and Lamiaa A. Elrefaei
Algorithms 2024, 17(7), 286; https://doi.org/10.3390/a17070286 (registering DOI) - 1 Jul 2024
Abstract
This work introduces an unsupervised framework for video anomaly detection, leveraging a hybrid deep learning model that combines a vision transformer (ViT) with a convolutional spatiotemporal relationship (STR) attention block. The proposed model addresses the challenges of anomaly detection in video surveillance by [...] Read more.
This work introduces an unsupervised framework for video anomaly detection, leveraging a hybrid deep learning model that combines a vision transformer (ViT) with a convolutional spatiotemporal relationship (STR) attention block. The proposed model addresses the challenges of anomaly detection in video surveillance by capturing both local and global relationships within video frames, a task that traditional convolutional neural networks (CNNs) often struggle with due to their localized field of view. We have utilized a pre-trained ViT as an encoder for feature extraction, which is then processed by the STR attention block to enhance the detection of spatiotemporal relationships among objects in videos. The novelty of this work is utilizing the ViT with the STR attention to detect video anomalies effectively in large and heterogeneous datasets, an important thing given the diverse environments and scenarios encountered in real-world surveillance. The framework was evaluated on three benchmark datasets, i.e., the UCSD-Ped2, CHUCK Avenue, and ShanghaiTech. This demonstrates the model’s superior performance in detecting anomalies compared to state-of-the-art methods, showcasing its potential to significantly enhance automated video surveillance systems by achieving area under the receiver operating characteristic curve (AUC ROC) values of 95.6, 86.8, and 82.1. To show the effectiveness of the proposed framework in detecting anomalies in extra-large datasets, we trained the model on a subset of the huge contemporary CHAD dataset that contains over 1 million frames, achieving AUC ROC values of 71.8 and 64.2 for CHAD-Cam 1 and CHAD-Cam 2, respectively, which outperforms the state-of-the-art techniques. Full article
(This article belongs to the Special Issue Algorithms for Image Processing and Machine Vision)
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26 pages, 15223 KiB  
Article
Construction of Soliton Solutions of Time-Fractional Caudrey–Dodd–Gibbon–Sawada–Kotera Equation with Painlevé Analysis in Plasma Physics
by Khadija Shakeel, Alina Alb Lupas, Muhammad Abbas, Pshtiwan Othman Mohammed, Farah Aini Abdullah and Mohamed Abdelwahed
Symmetry 2024, 16(7), 824; https://doi.org/10.3390/sym16070824 (registering DOI) - 1 Jul 2024
Abstract
Fractional calculus with symmetric kernels is a fast-growing field of mathematics with many applications in all branches of science and engineering, notably electromagnetic, biology, optics, viscoelasticity, fluid mechanics, electrochemistry, and signals processing. With the use of the Sardar sub-equation and the Bernoulli sub-ODE [...] Read more.
Fractional calculus with symmetric kernels is a fast-growing field of mathematics with many applications in all branches of science and engineering, notably electromagnetic, biology, optics, viscoelasticity, fluid mechanics, electrochemistry, and signals processing. With the use of the Sardar sub-equation and the Bernoulli sub-ODE methods, new trigonometric and hyperbolic solutions to the time-fractional Caudrey–Dodd–Gibbon–Sawada–Kotera equation have been constructed in this paper. Notably, the definition of our fractional derivative is based on the Jumarie’s modified Riemann–Liouville derivative, which offers a strong basis for our mathematical explorations. This equation is widely utilized to report a variety of fascinating physical events in the domains of classical mechanics, plasma physics, fluid dynamics, heat transfer, and acoustics. It is presumed that the acquired outcomes have not been documented in earlier research. Numerous standard wave profiles, such as kink, smooth bell-shaped and anti-bell-shaped soliton, W-shaped, M-shaped, multi-wave, periodic, bright singular and dark singular soliton, and combined dark and bright soliton, are illustrated in order to thoroughly analyze the wave nature of the solutions. Painlevé analysis of the proposed study is also part of this work. To illustrate how the fractional derivative affects the precise solutions of the equation via 2D and 3D plots. Full article
(This article belongs to the Special Issue Application of Symmetry in Equations)
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10 pages, 3589 KiB  
Article
Study on Grain Boundary Mechanical Behaviors of Polycrystalline γ-TiAl Using Molecular Dynamics Simulations
by Wenjuan Zhao, Maoqing He, Chunliang Li and Wei Chen
Metals 2024, 14(7), 779; https://doi.org/10.3390/met14070779 (registering DOI) - 1 Jul 2024
Abstract
In this study, the molecular dynamics (MD) method was used to study the tensile deformation of polycrystalline γ-TiAl with complex and random grain orientations. Firstly, the tensile deformation was simulated with different average grain sizes (8.60 nm, 6.18 nm, and 4.50 nm) and [...] Read more.
In this study, the molecular dynamics (MD) method was used to study the tensile deformation of polycrystalline γ-TiAl with complex and random grain orientations. Firstly, the tensile deformation was simulated with different average grain sizes (8.60 nm, 6.18 nm, and 4.50 nm) and strain rates (1 × 108 s−1, 5 × 108 s−1, and 1 × 109 s−1). The results show that the peak stress increases with an increase in tensile strain rate, and the peak stress decreases as the grain size decreases, showing an inverse Hall–Petch effect. Upon observing atomic configuration evolution during tensile deformation, it is found that the grain boundary is seriously distorted, which indicates obvious grain boundary sliding occurring. With a further increase in the loading, some dislocations nucleate at the grain boundaries and propagate towards the interior of the grains along the grain boundaries, which demonstrates that dislocation motion is the primary coordination of the mechanical process of the grain boundaries. The dislocation density near the grain boundaries continues to increase, leading to the generation of micro-cracks and eventually causing material failure. Another interesting phenomenon is that the grains rotate, and the specific rotation angle values of each grain are quantitatively calculated. Grain rotation relaxes the stress concentration near the grain boundaries and plays a toughening role. Consequently, the plastic deformation behaviors of polycrystalline γ-TiAl are achieved through the grain boundary mechanical process, that is, grain boundary sliding and grain rotation. Full article
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25 pages, 29087 KiB  
Article
HBIM for Conservation of Built Heritage
by Yahya Alshawabkeh, Ahmad Baik and Yehia Miky
ISPRS Int. J. Geo-Inf. 2024, 13(7), 231; https://doi.org/10.3390/ijgi13070231 (registering DOI) - 1 Jul 2024
Abstract
Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM [...] Read more.
Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation. Full article
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8 pages, 1431 KiB  
Article
Isotopic Differentiation (δ18OPO4) of Inorganic Phosphorus among Organic Wastes for Nutrient Runoff Tracing Studies: A Summary of the Literature with Refinement of Livestock Estimates for Grand Lake St. Marys Watershed (Ohio)
by Melanie M. Marshall, Stephen J. Jacquemin and Aubrey L. Jaqueth
Pollutants 2024, 4(3), 316-323; https://doi.org/10.3390/pollutants4030021 (registering DOI) - 1 Jul 2024
Abstract
The use of stable isotopes, specifically δ18OPO4 ratios, in differentiating potential sources of inorganic phosphorus (e.g., wastewater, septic, wild animals, domesticated animals, livestock, substrates, or commercial fertilizers) to watersheds is a growing field. This method produces data that, used in [...] Read more.
The use of stable isotopes, specifically δ18OPO4 ratios, in differentiating potential sources of inorganic phosphorus (e.g., wastewater, septic, wild animals, domesticated animals, livestock, substrates, or commercial fertilizers) to watersheds is a growing field. This method produces data that, used in conjunction with statistical mixing models, enables a better understanding of contributing sources of runoff. However, given the recent development of this research area there are obvious limitations that have arisen, due in large part to the limited available reference data to compare water samples. Here, we attempt to expand the availability of reference samples by applying stable isotope methods to three types of common agricultural manures: poultry, dairy, and swine. We also aim to concatenate the organic waste literature on this topic, creating a more robust comparison database for future study and application in phosphorus source partitioning research. Among our samples, δ18OPO4 ratios for poultry were considerably elevated compared to dairy and swine manures (values of 18.5‰, 16.5‰, and 17.9‰, respectively). Extending this to other published ratios of δ18OPO4 from various types of waste products (e.g., septic, wastewater, livestock, other animals), a total range from 8.7‰ to 23.1‰ emerged (with existing poultry manure samples also ranking among the highest overall). Variation among samples in the larger dataset demonstrates the need for a further compilation of δ18OPO4 ratios for various types of waste, especially specific to geographic regions and watershed scales. With an increased sample size, the statistical strength associated with these methods would greatly improve. Full article
(This article belongs to the Section Water Pollution)
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8 pages, 493 KiB  
Article
Sport-Specific Abdominal Wall Muscle Differences: A Comparative Study of Soccer and Basketball Players Using Ultrasonography
by Carlos Romero-Morales, Jorge Hugo Villafañe, Unai Torres, Diego Miñambres-Martín, Helios Pareja-Galeano, Isabel Rodríguez-Costa and Sergio L. Jiménez-Sáiz
Appl. Sci. 2024, 14(13), 5742; https://doi.org/10.3390/app14135742 (registering DOI) - 1 Jul 2024
Abstract
Aim: This study aims to compare the thickness of abdominal wall muscles—the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), rectus abdominis (RA), and inter-recti distance (IRD)—between amateur soccer and basketball players using ultrasonography. Methods: This cross-sectional study was conducted with 35 [...] Read more.
Aim: This study aims to compare the thickness of abdominal wall muscles—the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), rectus abdominis (RA), and inter-recti distance (IRD)—between amateur soccer and basketball players using ultrasonography. Methods: This cross-sectional study was conducted with 35 male amateur athletes, including 17 soccer players and 18 basketball players. Ultrasonographic measurements of the EO, IO, TrAb, RA muscles, and IRD were taken while the muscles were in a relaxed state for all the participants in both sides. Results: Significant differences were found in the RA muscle thickness, with basketball players showing a greater mean thickness compared to soccer players. No significant differences were observed in the TrAb, IO, and EO muscles between the two groups. The IRD showed a trend towards larger separation in basketball players, though this was not statistically significant. Conclusions: This study highlights sport-specific adaptations in the RA muscle, likely due to the distinct physical demands of basketball and soccer. The findings underscore the importance of tailored training and rehabilitation programs that consider these morphological differences to enhance performance and reduce injury risks. Full article
(This article belongs to the Special Issue Biomechanics and Sport Engineering: Latest Advances and Prospects)
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19 pages, 746 KiB  
Article
Fast Depth Map Coding Algorithm for 3D-HEVC Based on Gradient Boosting Machine
by Xiaoke Su, Yaqiong Liu and Qiuwen Zhang
Electronics 2024, 13(13), 2586; https://doi.org/10.3390/electronics13132586 (registering DOI) - 1 Jul 2024
Abstract
Three-Dimensional High-Efficiency Video Coding (3D-HEVC) has been extensively researched due to its efficient compression and deep image representation, but encoding complexity continues to pose a difficulty. This is mainly attributed to redundancy in the coding unit (CU) recursive partitioning process and rate–distortion (RD) [...] Read more.
Three-Dimensional High-Efficiency Video Coding (3D-HEVC) has been extensively researched due to its efficient compression and deep image representation, but encoding complexity continues to pose a difficulty. This is mainly attributed to redundancy in the coding unit (CU) recursive partitioning process and rate–distortion (RD) cost calculation, resulting in a complex encoding process. Therefore, enhancing encoding efficiency and reducing redundant computations are key objectives for optimizing 3D-HEVC. This paper introduces a fast-encoding method for 3D-HEVC, comprising an adaptive CU partitioning algorithm and a rapid rate–distortion-optimization (RDO) algorithm. Based on the ALV features extracted from each coding unit, a Gradient Boosting Machine (GBM) model is constructed to obtain the corresponding CU thresholds. These thresholds are compared with the ALV to further decide whether to continue dividing the coding unit. The RDO algorithm is used to optimize the RD cost calculation process, selecting the optimal prediction mode as much as possible. The simulation results show that this method saves 52.49% of complexity while ensuring good video quality. Full article
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19 pages, 6299 KiB  
Article
Anomaly Detection of Sensor Arrays of Underwater Methane Remote Sensing by Explainable Sparse Spatio-Temporal Transformer
by Kai Zhang, Wangze Ni, Yudi Zhu, Tao Wang, Wenkai Jiang, Min Zeng and Zhi Yang
Remote Sens. 2024, 16(13), 2415; https://doi.org/10.3390/rs16132415 (registering DOI) - 1 Jul 2024
Abstract
The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique [...] Read more.
The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66). Full article
(This article belongs to the Special Issue Advances of Underwater Remote Sensing of Methane: Spatiotemporal Distribution)
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16 pages, 1643 KiB  
Article
Steam Explosion Pretreatment of Polysaccharide from Hypsizygus marmoreus: Structure and Antioxidant Activity
by Zirong Huang, Yueyue Qiang, Shiyu Zhang, Yujia Ou, Zebin Guo and Baodong Zheng
Foods 2024, 13(13), 2086; https://doi.org/10.3390/foods13132086 (registering DOI) - 1 Jul 2024
Abstract
This paper investigated the effects of steam explosion (SE) pretreatment on the structural characteristics and antioxidant activity of Hypsizygus marmoreus polysaccharides (HPS). Hypsizygus marmoreus samples were pretreated at different SE temperatures (120–200 °C) and polysaccharides were extracted using the water extraction and alcohol [...] Read more.
This paper investigated the effects of steam explosion (SE) pretreatment on the structural characteristics and antioxidant activity of Hypsizygus marmoreus polysaccharides (HPS). Hypsizygus marmoreus samples were pretreated at different SE temperatures (120–200 °C) and polysaccharides were extracted using the water extraction and alcohol precipitation method. The results showed that SE pretreatment improved the extraction rate of HPS. Under the conditions of SE treatment time of 60 s and temperature of 160 °C, the extraction rate of HPS was the highest (8.78 ± 0.24%). After SE pretreatment, the structural changes of HPS tended to enhance the antioxidant activity, which showed that the content of Gal and Man in the monosaccharide composition increased and the molecular weight decreased. When testing antioxidant activity in vitro, the ability of SE-pretreated HPS to scavenge DPPH radicals, hydroxyl radicals, and superoxide anion radicals was better than that of HPS without SE pretreatment. Our findings shed light on SE pretreatment as an efficient method for extracting active polysaccharides, providing a new way to improve their extraction rate and biological activity. Full article
(This article belongs to the Section Food Engineering and Technology)
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16 pages, 4323 KiB  
Article
Processing and Mechanics of Aromatic Vitrimeric Composites at Elevated Temperatures and Healing Performance
by Tanaya Mandal, Unal Ozten, Louis Vaught, Jacob L. Meyer, Ahmad Amiri, Andreas Polycarpou and Mohammad Naraghi
J. Compos. Sci. 2024, 8(7), 252; https://doi.org/10.3390/jcs8070252 (registering DOI) - 1 Jul 2024
Abstract
Carbon fiber reinforced polymer (CFRP) composites are renowned for their exceptional mechanical properties, with applications in industries such as automotive, aerospace, medical, civil, and beyond. Despite these merits, a significant challenge in CFRPs lies in their repairability and maintenance. This study, for the [...] Read more.
Carbon fiber reinforced polymer (CFRP) composites are renowned for their exceptional mechanical properties, with applications in industries such as automotive, aerospace, medical, civil, and beyond. Despite these merits, a significant challenge in CFRPs lies in their repairability and maintenance. This study, for the first time, delves into the processing and self-healing capability of aromatic thermosetting co-polyester vitrimer-based carbon fiber composites through mechanical testing. Vitrimers are an emerging class of thermosetting polymers, which, owing to their exchangeable covalent bonds, enable the re-formation of bonds across cracks. The specific vitrimer chosen for this study is an aromatic thermosetting co-polyester (ATSP). The mechanical properties of samples were analyzed initially through three-point bending (3PB) testing at room temperature before and after healing (by curing samples for 2 h at 280 °C). Samples were also 3PB tested at 100 °C to analyze their mechanical properties at an elevated temperature for comparison to the samples tested at room temperature. To investigate the fracture properties, optical microscopy images of samples were taken after 3PB tests, which were analyzed to observe crack initiation and crack growth behavior. Through load–displacement curves from double cantilever beam (DCB) mechanical testing, the Mode I crack initiation fracture toughness values of self-healed composites and control composites were calculated to evaluate healing efficiency in ATSP CFRP composites cured at 280 °C for 2 h. Scanning electron microscopy (SEM) showed a similar surface morphology of cracks before and after self-healing. Micro-computed tomography (CT) X-ray imaging confirmed that the healed samples closely resembled the as-fabricated ones, with the exception of some manufacturing voids, caused by outgassing in the initial healing cycle. This research demonstrated the ability for the in situ repair of ATSP CFRPs by restoring the fracture toughness to values comparable to the pristine composite (~289 J/m2). Full article
(This article belongs to the Special Issue Carbon Fiber Composites, Volume III)
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20 pages, 8876 KiB  
Article
A Comprehensive Survey on High-Definition Map Generation and Maintenance
by Kaleab Taye Asrat and Hyung-Ju Cho
ISPRS Int. J. Geo-Inf. 2024, 13(7), 232; https://doi.org/10.3390/ijgi13070232 (registering DOI) - 1 Jul 2024
Abstract
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully [...] Read more.
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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10 pages, 4982 KiB  
Article
Eco-Friendly Isolated Nanocellulose from Seaweed Biomass via Modified-Acid and Electron Beam Process for Biodegradable Polymer Composites
by Jae-Hun Kim, Jin-Ju Jeong and Jung-Soo Lee
J. Compos. Sci. 2024, 8(7), 253; https://doi.org/10.3390/jcs8070253 (registering DOI) - 1 Jul 2024
Abstract
Nanocellulose (NC) has emerged as a promising biodegradable material with applications in various industrial fields owing to its high mechanical strength, thermal stability, and eco-friendly properties. Traditional methods for isolating NC from wood-based biomass (WB) involve high energy consumption and extensive chemical usage, [...] Read more.
Nanocellulose (NC) has emerged as a promising biodegradable material with applications in various industrial fields owing to its high mechanical strength, thermal stability, and eco-friendly properties. Traditional methods for isolating NC from wood-based biomass (WB) involve high energy consumption and extensive chemical usage, leading to environmental and sustainability concerns. This study explored an alternative approach to isolate NC from seaweed-based biomass (SB) (SNC), which contains fewer non-cellulosic components and a higher cellulose content than WB, thereby yielding a more efficient e-isolation process. We employed a combination of modified-acid solution and electron beam (E-beam) technology to isolate NC from SB. The E-beam process enhanced the crystallinity while reducing the particle size, thus facilitating NC isolation with reduced environmental impact and processing time. Moreover, our method significantly reduced the need for harsh chemical reagents and energy-intensive processes, which are typically associated with traditional NC isolation methods. We fabricated biodegradable films with improved mechanical properties using NC as a reinforcing agent in polymer composites, thereby demonstrating the potential of NC-based materials for various applications. Therefore, our proposed approach offers a sustainable and efficient method for NC isolation and serves as a guide for the development of eco-friendly industrial processes. Our findings contribute to ongoing efforts to create sustainable materials and reduce the environmental footprint of the manufacturing industry. Full article
(This article belongs to the Special Issue Sustainable Biocomposites, Volume II)
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