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36 pages, 1445 KiB

Open AccessArticle

Digital Twin Framework for Aircraft Lifecycle Management Based on Data-Driven Models

by Igor Kabashkin

Mathematics 2024, 12(19), 2979; https://doi.org/10.3390/math12192979 (registering DOI) - 25 Sep 2024

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G [...] Read more.

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. Full article

(This article belongs to the Special Issue Statistical Modeling and Data-Driven Methods in Aviation Systems)

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19 pages, 5063 KiB

Open AccessArticle

An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization

by Sang-Bo Sim and Hyung-Jun Kim

Water 2024, 16(19), 2726; https://doi.org/10.3390/w16192726 (registering DOI) - 25 Sep 2024

Abstract

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, leading to more frequent flood damage. Consequently, there is a need for a rapid and accurate analysis of urban flood response capabilities. The dual-drainage model has been [...] Read more.

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, leading to more frequent flood damage. Consequently, there is a need for a rapid and accurate analysis of urban flood response capabilities. The dual-drainage model has been widely used for accurate flood analysis, with minimum time step synchronization being commonly adopted. However, this method has limitations in terms of speed. This study applied the hyper-connected solution for an urban flood (HC-SURF) model with fixed-time step flow synchronization, validated its accuracy using laboratory observation data, and tested its effectiveness in real urban watersheds with various synchronization times. Excellent performance was achieved in simulating real phenomena. In actual urban watersheds, as the synchronization time increased, the errors in surcharge and discharge also increased due to the inability to accurately reflect water level changes within the synchronization time; however, overall, they remained minimal. Therefore, the HC-SURF model is demonstrated as a useful tool for urban flood management that can be used to advantage in real-time flood forecasting and decision-making. Full article

(This article belongs to the Topic Urban Hydrogeology Research)

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17 pages, 6007 KiB

Open AccessArticle

An Improved Unfolded Coprime Linear Array Design for DOA Estimation with No Phase Ambiguity

by Pan Gong and Xiaofei Zhang

Sensors 2024, 24(19), 6205; https://doi.org/10.3390/s24196205 (registering DOI) - 25 Sep 2024

Abstract

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of [...] Read more.

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Robust Parameter Estimation with Sensor Arrays in Complex Electromagnetic Environments)

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13 pages, 578 KiB

Open AccessArticle

Predictors of Poor Mental Health Outcomes in Healthcare Workers during COVID-19: A Two Waves Study

by Emanuela Saveria Gritti, Giulia Bassi, Arianna Schiano Lomoriello, Alessandra Simonelli, Silvia Salcuni, Tommaso Boldrini and Paolo Girardi

Healthcare 2024, 12(19), 1921; https://doi.org/10.3390/healthcare12191921 (registering DOI) - 25 Sep 2024

Abstract

Objective: This cross-sectional study aimed to identify potential predictors of poor mental health outcomes among healthcare workers in two different waves of the COVID-19 emergency in Italy. Methods: An online survey collected data from N = 557 healthcare workers (21–77 years). The study [...] Read more.

Objective: This cross-sectional study aimed to identify potential predictors of poor mental health outcomes among healthcare workers in two different waves of the COVID-19 emergency in Italy. Methods: An online survey collected data from N = 557 healthcare workers (21–77 years). The study predictors were sociodemographic characteristics, occupational status, factors related to the work environment, COVID-19-related adverse events, and lifetime traumatic events. The poor mental health outcomes that were considered were depersonalization/derealization, anxiety, depression, and somatization symptoms. Results: The main predictors of poor mental health outcomes were sleeping less than six hours per night, inadequate protective equipment measures, female gender, personal and familiar infection, living alone, working as a nurse, and working in a COVID-19 ward. Healthcare workers in 2021 reported experiencing more serious accidents and stressful events than those of the first wave. Depressive symptoms and COVID-19-related adverse events were higher in the second pandemic outbreak than in the first. Conclusions: Preventive strategies against poor mental health outcomes should be particularly focused on female nurses who live alone, work in areas with high infection rates, and have experienced the COVID-19 infection personally or who are close to people that have experienced the infection. Full article

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10 pages, 1085 KiB

Open AccessArticle

Multiple Herbicide Resistance in Annual Ryegrass (Lolium rigidum Gaudin) in the Southeastern Cropping Region of Australia

by Gulshan Mahajan and Bhagirath Singh Chauhan

Agronomy 2024, 14(10), 2206; https://doi.org/10.3390/agronomy14102206 (registering DOI) - 25 Sep 2024

Abstract

Annual ryegrass (Lolium rigidum) is a problematic weed in winter crops and fallows in the southeastern cropping region (SCR) of Australia. This weed has evolved resistance to multiple herbicide groups, globally. In Australia, L. rigidum is more prevalent in the western [...] Read more.

Annual ryegrass (Lolium rigidum) is a problematic weed in winter crops and fallows in the southeastern cropping region (SCR) of Australia. This weed has evolved resistance to multiple herbicide groups, globally. In Australia, L. rigidum is more prevalent in the western and southern regions than in SCR. To assess the herbicide resistance status of L. rigidum, the response of five L. rigidum populations (collected from the SCR) to glyphosate, glufosinate, paraquat, haloxyfop-P-ethyl, and clethodim is determined using dose–response curves. Three parametric logistic models are used to determine the herbicide dose required to achieve 50% survival (LD₅₀) and 50% growth reduction (GR₅₀). The LD₅₀ values for 50% survival at 28 days after treatment range from 1702 g a.e. ha⁻¹ to 8225 g a.e. ha⁻¹ for glyphosate, 1637 g a.i. ha⁻¹ to 1828 g a.i. ha⁻¹ for glufosinate, 141 g a.i. ha⁻¹ to 307 g a.i. ha⁻¹ for paraquat, 11 g a.i. ha⁻¹ to 107 g a.i. ha⁻¹ for haloxyfop-P-ethyl, and 17 g a.i. ha⁻¹ to 48 g a.i. ha⁻¹ for clethodim. The resistance factor, based on GR₅₀ value, is highest in the S7 population (2.2 times) for glyphosate, the S11 population (2.3 times) for glufosinate, the S11 population (2.0 time) for paraquat, the S7 population (3.9 times) for haloxyfop-P-ethyl, and the S3 population (3.1 times) for clethodim, compared with the susceptible or less tolerant population. The S11 population is found to be resistant to five tested herbicides, based on resistance factors. Similarly, the S3 population is highly resistant to glyphosate, haloxyfop-P-ethyl, and clethodim compared with the W4 population. These results suggest that L. rigidum populations in the SCR exhibit resistance to multiple herbicide groups at labelled field rates. The findings highlight the necessity of adopting an integrated management approach, including the use of residual herbicides, tank mixing herbicides with different modes of action, and rotating herbicides in conjunction with cultural and mechanical control methods. Full article

(This article belongs to the Special Issue Herbicides and Chemical Control of Weeds)

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9 pages, 1490 KiB

Open AccessArticle

Evaluating Generative AI’s Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets

by Ethan Hillis, Kriti Bhattarai and Zachary Abrams

J. Pers. Med. 2024, 14(10), 1022; https://doi.org/10.3390/jpm14101022 (registering DOI) - 25 Sep 2024

Abstract

Background: Genetic data play a crucial role in diagnosing and treating various diseases, reflecting a growing imperative to integrate these data into clinical care. However, significant barriers such as the structure of electronic health records (EHRs), insurance costs for genetic testing, and the [...] Read more.

Background: Genetic data play a crucial role in diagnosing and treating various diseases, reflecting a growing imperative to integrate these data into clinical care. However, significant barriers such as the structure of electronic health records (EHRs), insurance costs for genetic testing, and the interpretability of genetic results impede this integration. Methods: This paper explores solutions to these challenges by combining recent technological advances with informatics and data science, focusing on the diagnostic potential of artificial intelligence (AI) in cancer research. AI has historically been applied in medical research with limited success, but recent developments have led to the emergence of large language models (LLMs). These transformer-based generative AI models, trained on vast datasets, offer significant potential for genetic and genomic analyses. However, their effectiveness is constrained by their training on predominantly human-written text rather than comprehensive, structured genetic datasets. Results: This study reevaluates the capabilities of LLMs, specifically GPT models, in performing supervised prediction tasks using structured gene expression data. By comparing GPT models with traditional machine learning approaches, we assess their effectiveness in predicting cancer subtypes, demonstrating the potential of AI models to analyze real-world genetic data for generating real-world evidence. Full article

(This article belongs to the Special Issue AI and Precision Medicine: Innovations and Applications)

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17 pages, 5615 KiB

Open AccessArticle

Sex-Specific Lipid Profiles and Flavor Volatiles in Giant Salamander (Andrias davidianus) Tails Revealed by Lipidomics and GC-IMS

by Shibo Zhao, Jinghong Yu, Linjie Xi, Xiangdong Kong, Jinjin Pei, Pengfei Jiang, Ruichang Gao and Wengang Jin

Foods 2024, 13(19), 3048; https://doi.org/10.3390/foods13193048 (registering DOI) - 25 Sep 2024

Abstract

To elucidate the relationships between lipid components and odor traits, this study comparatively characterized the distinct lipid compositions and flavor volatiles in giant salamander tails of different sexes via mass-spectrometry-based lipidomics and GC-IMS. A total of 3145 fat metabolites were detected in male [...] Read more.

To elucidate the relationships between lipid components and odor traits, this study comparatively characterized the distinct lipid compositions and flavor volatiles in giant salamander tails of different sexes via mass-spectrometry-based lipidomics and GC-IMS. A total of 3145 fat metabolites were detected in male and female giant salamander tails, with the largest contributors being triglycerides (TGs, 840) and phosphatidylcholines (PCs, 383). Notably, the contents of PCs and TGs were greater in female tails than in male tails, and the levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were also greater in the female group. Additionally, a total of 45 volatile components were detected, namely, 14 aldehydes, 14 alcohols, 9 ketones, 3 acids, 3 esters, 1 ether, and 1 amine. Alcohols (29.96% to 34.85%) and aldehydes (21.07% to 22.75%) were the predominant volatiles. Multivariate statistical analysis revealed 22 key differential fats and 26 differential odor substances as distinguishing labels between sexes. Correlation analysis revealed that the concentrations of triethylamine, dimethyl sulfide, ethanol-D, and 3-methyl butanal-D were significantly positively correlated with the concentrations of diglyceride (DG) (26:6e), cardiolipin (CL) (59:4), acylcarnitine (AcCa) (22:4), and triglyceride (TG) (52:10) (p < 0.01). Threefold cross-validation revealed that the prediction accuracies of these differential lipids and volatile compounds for sex recognition via the random forest model were 100%. These findings might not only provide insight into the effects of sexes on the lipid and volatile profiles of giant salamander tails but also provide clues for their gender recognition. Full article

(This article belongs to the Special Issue Current Status and Future Perspectives in Flavor Chemistry and Technology in the Food Industry)

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13 pages, 1175 KiB

Open AccessArticle

Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

by Qi Ge, Yijie Xia, Junwei Shu, Jin Li and Hongyue Sun

J. Mar. Sci. Eng. 2024, 12(10), 1701; https://doi.org/10.3390/jmse12101701 (registering DOI) - 25 Sep 2024

Abstract

Accurate prediction of the compression index (c_c) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining

c_{c}

are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning [...] Read more.

Accurate prediction of the compression index (c_c) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining

c_{c}

are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the

c_{c}

of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e₀), and natural water content w—were leveraged for accurate

c_{c}

prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (

R^{2}

) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with

e_{0}

identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments. Full article

(This article belongs to the Special Issue Advances in Marine Engineering: Geological Environment and Hazards—3rd Edition)

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14 pages, 7651 KiB

Open AccessArticle

Optimization of Motor Rotor Punch Wear Parameters Based on Response Surface Method

by Shaobo Wen, Ran She, Zhendong Zhao and Yipeng Gong

Machines 2024, 12(10), 671; https://doi.org/10.3390/machines12100671 (registering DOI) - 25 Sep 2024

Abstract

To reduce the wear of the motor rotor punching punch and ensure the efficiency is the highest in actual production, the finite element analysis software Deform-3Dv11 is used to simulate the punch wear based on the Archard model theory. With punch wear as [...] Read more.

To reduce the wear of the motor rotor punching punch and ensure the efficiency is the highest in actual production, the finite element analysis software Deform-3Dv11 is used to simulate the punch wear based on the Archard model theory. With punch wear as the response target and punch speed, punch clearance, and punch edge fillet as the main factors, 17 groups of response surface Box–Behnken test designs are established, as well as a quadratic polynomial regression model between the main factors and the response. The results revealed that: the influence of various parameters on punch wear is in the order of punch edge fillet C > punch clearance B > punch speed A; the order of the interactive influence of various factors is as follows: punch speed and punch edge fillet AC > punch speed and punch clearance AB > punch clearance and punch edge fillet BC. The optimal blanking process combination obtained by using Design-Expert13 software is as follows: blanking speed 50 mm/s, blanking clearance 0.036 mm, and die cutting edge rounded corner 0.076 mm; the predicted response surface value is 6.95 × 10⁻¹² mm. Through simulation verification, the actual optimized simulation value is 6.93 × 10⁻¹² mm, with an absolute relative error of 2.5% for the predicted response value. Moreover, the optimized simulation value is reduced by 30.4% compared to the one before optimization, effectively reducing the punch wear of the motor rotor punching forming and providing a theoretical foundation for further wear optimization. Full article

(This article belongs to the Special Issue Advances in Design and Manufacturing in Die Casting and Metal Forming)

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23 pages, 36997 KiB

Open AccessArticle

Enhanced Monitoring of Sub-Seasonal Land Use Dynamics in Vietnam’s Mekong Delta through Quantile Mapping and Harmonic Regression

by Nick Kupfer, Tuan Quoc Vo, Felix Bachofer, Juliane Huth, Harry Vereecken, Lutz Weihermüller and Carsten Montzka

Remote Sens. 2024, 16(19), 3569; https://doi.org/10.3390/rs16193569 (registering DOI) - 25 Sep 2024

Abstract

In response to economic and environmental challenges like sea-level rise, salinity intrusion, groundwater extraction, sand mining, and sinking delta phenomena, the demand for solutions to adapt to changing conditions in riverine environments has increased significantly. High-quality analyses of land use and land cover [...] Read more.

In response to economic and environmental challenges like sea-level rise, salinity intrusion, groundwater extraction, sand mining, and sinking delta phenomena, the demand for solutions to adapt to changing conditions in riverine environments has increased significantly. High-quality analyses of land use and land cover (LULC) dynamics play a critical role in addressing these challenges. This study introduces a novel high-spatial resolution satellite-based approach to identify sub-seasonal LULC dynamics in the Mekong River Delta (MRD), employing a three-year (2021–2023) Sentinel-1 and Sentinel-2 satellite data time series. The primary obstacle is discerning detailed vegetation dynamics, particularly the seasonality of rice crops, answered through quantile mapping, harmonic regression with Fourier transform, and phenological metrics as inputs to a random forest machine learning classifier. Due to the substantial data volume, Google’s cloud computing platform Earth Engine was utilized for the analysis. Furthermore, the study evaluated the relative significance of various input features. The overall accuracy of the classification is 82.6% with a kappa statistic of 0.81, determined using comprehensive reference data collected in Vietnam. While the purely pixel-based approach has limitations, it proves to be a viable method for high-spatial resolution satellite image time series classification of the MRD. Full article

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16 pages, 6645 KiB

Open AccessReview

Highly Stretchable Composite Conductive Fibers (SCCFs) and Their Applications

by Diane Tang, Ruixiang Qu, Huacui Xiang, Enjian He, Hanshi Hu, Zhijun Ma, Guojun Liu, Yen Wei and Jiujiang Ji

Polymers 2024, 16(19), 2710; https://doi.org/10.3390/polym16192710 (registering DOI) - 25 Sep 2024

Abstract

Stretchable composite conductive fibers (SCCFs) exhibit remarkable conductivity, stretchability, breathability, and biocompatibility, making them ideal candidates for wearable electronics and bioelectronics. The exploitation of SCCFs in electronic devices requires a careful balance of many aspects, including material selection and process methodologies, to address [...] Read more.

Stretchable composite conductive fibers (SCCFs) exhibit remarkable conductivity, stretchability, breathability, and biocompatibility, making them ideal candidates for wearable electronics and bioelectronics. The exploitation of SCCFs in electronic devices requires a careful balance of many aspects, including material selection and process methodologies, to address the complex challenges associated with their electrical and mechanical properties. In this review, we elucidate the conductive mechanism of SCCFs and summarize strategies for integrating various conductors with stretchable fibers, emphasizing the primary challenges in fabricating highly conductive fibers. Furthermore, we explore the multifaceted applications of SCCFs-based frameworks in wearable electronic devices. This review aims to emphasize the significance of SCCFs and offers insights into their conductive mechanisms, material selection, manufacturing technologies, and performance improvement. Hopefully, it can guide the innovative development of SCCFs and broaden their application potential. Full article

(This article belongs to the Section Innovation of Polymer Science and Technology)

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18 pages, 3488 KiB

Open AccessArticle

Adsorption of Ciprofloxacin and Lidocaine by Non-Fibrous Raw Mg-Clays: The Role of Composition and Texture

by Maria Eugenia Roca-Jalil, Telma Musso, Vanina Rodriguez-Ameijide, Micaela Sanchez, Andrea Maggio, Miria Teresita Baschini, Gisela Pettinari, Luis Villa, Manuel Pozo and Alejandro Pérez-Abad

Minerals 2024, 14(10), 966; https://doi.org/10.3390/min14100966 (registering DOI) - 25 Sep 2024

Abstract

This study evaluated non-fibrous Mg-clays as potential adsorbents of emerging contaminants (ECs) from water. The materials were characterized, and their textural and structural properties were related to their ability to remove two model EC molecules: ciprofloxacin (CPX) and lidocaine (LID). The results showed [...] Read more.

This study evaluated non-fibrous Mg-clays as potential adsorbents of emerging contaminants (ECs) from water. The materials were characterized, and their textural and structural properties were related to their ability to remove two model EC molecules: ciprofloxacin (CPX) and lidocaine (LID). The results showed that Ad-6 and Ad-7 are mixed-layer kerolite/stevensite, while Ad-5 and Ad-8 are mainly composed of smectite minerals like stevensite and saponite, respectively. Ad-8 exhibited the highest CPX-adsorption capacity (0.91 mmol·g⁻¹ clay), likely due to its saponite content. Mixed-layer materials also performed well, with Ad-6 and Ad-7 achieving an adsorption capacity of 0.8 and 0.55 mmol·g⁻¹ clay, respectively. Adsorption studies suggested that CPX is adsorbed through ion exchange in materials with high smectite content (Ad-8 and Ad-5), while interstratified materials showed enhanced retention due to kerolite presence, which improves their porous structures. Similar findings were observed for LID, indicating a cationic-exchange mechanism for LID adsorption in all the materials and suggesting that the molecular size of the EC could regulate the removal capacity of these materials. This work showed that the studied Mg-clays could be effectively used for the removal of pharmaceutical pollutants, expanding their commercial possibilities. Full article

(This article belongs to the Special Issue Mineralogical and Geochemical Characterization of Geological Materials)

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21 pages, 991 KiB

Open AccessReview

Contamination with Pharmaceuticals in Aquatic Environment: Focus on Analytical Methodologies

by Agneša Szarka, Lucia Vnuková, Zuzana Keršňáková, Nicolette Viktoryová and Svetlana Hrouzková

Appl. Sci. 2024, 14(19), 8645; https://doi.org/10.3390/app14198645 (registering DOI) - 25 Sep 2024

Abstract

The growing prevalence of pharmaceutical compounds in the environment raises significant concerns due to their potential impacts on ecological and human health. This present manuscript focuses on the methods used to extract and determine these pharmaceuticals in water samples. It provides a comprehensive [...] Read more.

The growing prevalence of pharmaceutical compounds in the environment raises significant concerns due to their potential impacts on ecological and human health. This present manuscript focuses on the methods used to extract and determine these pharmaceuticals in water samples. It provides a comprehensive analysis of the extraction techniques and analytical approaches employed for the identification and quantification of pharmaceuticals in environmental water. Due to their chemical properties and widespread use, pharmaceuticals persist in the environment and contaminate water bodies, soil, and sediments. The presence of pharmaceuticals in the aquatic environment has been linked to several adverse effects on aquatic organisms, including the disruption of physiological processes and reproductive impairment. Furthermore, pharmaceuticals in the environment can affect human health through food and drinking water contamination and contribute to antibiotic resistance. The analysis of pharmaceutical contaminants in water samples presents several challenges due to the complex matrix and low concentrations of target substances. Various sample preparation techniques and protocols, including solid-phase extraction (more than 76% of the studied literature) and QuEChERS (quick, easy, cheap, effective, rugged, and safe), coupled with liquid chromatography–tandem-mass spectrometry, are commonly used for their determination. These methods offer high sensitivity, selectivity, and efficiency in identifying and quantifying pharmaceuticals in environmental samples. It is, therefore, essential that ongoing research is conducted in order to develop more efficient analytical methods and mitigation strategies to address pharmaceutical contamination in the environmental water effectively. It is also crucial that increased awareness and regulatory measures are put in place in order to minimize the environmental and human health risks associated with pharmaceutical pollutants. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Remove of Pollutants for Green and Healthy Environment)

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19 pages, 10874 KiB

Open AccessArticle

The Effects of Mechanical Loading on Resonant Response of a Conformal Load-Bearing Antenna System

by Shouxun Lu, Kelvin J. Nicholson, Joel Patniotis, John Wang and Wing Kong Chiu

Sensors 2024, 24(19), 6206; https://doi.org/10.3390/s24196206 (registering DOI) - 25 Sep 2024

Abstract

Glass fibre-reinforced polymer (GFRP) is a suitable substrate material for constructing a Conformal Load-Bearing Antenna Structure (CLAS). The relative permittivity of the CLAS substrate, which determines its resonant frequency, is affected by damage sustained by GFRP. This paper investigates the effects of damage [...] Read more.

Glass fibre-reinforced polymer (GFRP) is a suitable substrate material for constructing a Conformal Load-Bearing Antenna Structure (CLAS). The relative permittivity of the CLAS substrate, which determines its resonant frequency, is affected by damage sustained by GFRP. This paper investigates the effects of damage (induced by mechanically loading the substrate) on the resonant response of the CLAS. Decoupling the antenna from the substrate was essential to evaluate the CLAS’s true response to the induced damage. This paper details a systematic investigation examining how the frequency response of a “pristine” antenna and a surface-mounted antenna respond to a substrate subjected to quasi-statically induced mechanical damage and cyclic fatigue loading. The results demonstrate that the resonant frequency of the CLAS varies as a function of the substrate’s mechanical damage. The prepared CLAS is tolerant to a certain degree of mechanical loading and related damage with its resonant frequency remaining within an acceptable bandwidth. Full article

(This article belongs to the Section Physical Sensors)

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17 pages, 8112 KiB

Open AccessArticle

Silicon Nanomaterials Enhance Seedling Growth and Plant Adaptation to Acidic Soil by Promoting Photosynthesis and Antioxidant Activity in Mustard (Brassica campestris L.)

by Md. Kamrul Hasan, Jannat Shopan, Israt Jahan and Tonima Islam Suravi

Int. J. Mol. Sci. 2024, 25(19), 10318; https://doi.org/10.3390/ijms251910318 (registering DOI) - 25 Sep 2024

Abstract

Soil acidity is a divesting factor that restricts crop growth and productivity. Conversely, silicon nanomaterials (Si-NMs) have been praised as a blessing of modern agricultural intensification by overcoming the ecological barrier. Here, we performed a sequential study from seed germination to the yield [...] Read more.

Soil acidity is a divesting factor that restricts crop growth and productivity. Conversely, silicon nanomaterials (Si-NMs) have been praised as a blessing of modern agricultural intensification by overcoming the ecological barrier. Here, we performed a sequential study from seed germination to the yield performance of mustard (Brassica campestris) crops under acid-stressed conditions. The results showed that Si-NMs significantly improved seed germination and seedling growth under acid stress situations. These might be associated with increased antioxidant activity and the preserve ratio of GSH/GSSG and AsA/DHA, which is restricted by soil acidity. Moreover, Si-NMs in field regimes significantly diminished the acid-stress-induced growth inhibitions, as evidenced by increased net photosynthesis and biomass accumulations. Again, Si-NMs triggered all the critical metrics of crop productivity, including the seed oil content. Additionally, Si-NMs, upon dolomite supplementation, further triggered all the metrics of yields related to farming resilience. Therefore, the present study highlighted the crucial roles of Si-NMs in sustainable agricultural expansion and cropping intensification, especially in areas affected by soil acidity. Full article

(This article belongs to the Special Issue Advance in Plant Abiotic Stress: 2nd Edition)

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14 pages, 8733 KiB

Open AccessArticle

Determining the Effect of Non-Thermal Plasma on the Transmembrane Kinetics of Melittin through Molecular Explorations

by Yanxiu Cui, Tong Zhao, Yanxiong Niu, Xiaolong Wang and Yuantao Zhang

Biomolecules 2024, 14(10), 1207; https://doi.org/10.3390/biom14101207 (registering DOI) - 25 Sep 2024

Abstract

Non-thermal plasma (NTP) synergistic anticancer strategies are a current hotspot of interest at the intersection of plasma biomedicine. Melittin (MEL) has been shown to inhibit cancer in many malignant tumors; however, its clinical application is controversial. Therefore, the transmembrane process and mechanism of [...] Read more.

Non-thermal plasma (NTP) synergistic anticancer strategies are a current hotspot of interest at the intersection of plasma biomedicine. Melittin (MEL) has been shown to inhibit cancer in many malignant tumors; however, its clinical application is controversial. Therefore, the transmembrane process and mechanism of MEL activity in different cell systems were studied and the combination of MEL and NTP was proposed in this paper. The results showed that the electrostatic attraction between MEL and the lipid bilayer contributes to the stable orientation of MEL on the membrane surface. In addition, sialic acid overexpression affects the degree to which MEL binds the membrane system and the stability of the membrane structure. The use of NTP to reduce the dosage of MEL and its related nonspecific cytolysis activity has certain clinical application value. The results of this study provide theoretical support for improving the clinical applicability of MEL and contribute to the further development of plasma biomedicine. Full article

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18 pages, 5942 KiB

Open AccessArticle

An Efficient Fabrication Approach for Multi-Cancer Responsive Chemoimmuno Co-Delivery Nanoparticles

by Jianxi Huang, Yu-Ting Chien, Qingxin Mu and Miqin Zhang

Pharmaceutics 2024, 16(10), 1246; https://doi.org/10.3390/pharmaceutics16101246 (registering DOI) - 25 Sep 2024

Abstract

Background/Objectives: Cancer remains one of the leading causes of death, with breast, liver, and pancreatic cancers significantly contributing to this burden. Traditional treatments face issues including dose-limiting toxicity, drug resistance, and limited efficacy. Combining therapeutic agents can enhance effectiveness and reduce toxicity, but [...] Read more.

Background/Objectives: Cancer remains one of the leading causes of death, with breast, liver, and pancreatic cancers significantly contributing to this burden. Traditional treatments face issues including dose-limiting toxicity, drug resistance, and limited efficacy. Combining therapeutic agents can enhance effectiveness and reduce toxicity, but separate administration often leads to inefficiencies due to differing pharmacokinetics and biodistribution. Co-formulating hydrophobic chemotherapeutics such as paclitaxel (PTX) and hydrophilic immunologic agents such as polyinosinic-polycytidylic acid (Poly IC) is particularly challenging due to their distinct physicochemical properties. This study presents a novel and efficient approach for the co-delivery of PTX and Poly IC using chitosan-based nanoparticles. Method: Chitosan-PEG (CP) nanoparticles were developed to encapsulate both PTX and Poly IC, overcoming their differing physicochemical properties and enhancing therapeutic efficacy. Results: With an average size of ~100 nm, these nanoparticles facilitate efficient cellular uptake and stability. In vitro results showed that CP-PTX-Poly IC nanoparticles significantly reduced cancer cell viability in breast (4T1), liver (HepG2), and pancreatic (Pan02) cancer types, while also enhancing dendritic cell (DC) maturation. Conclusions: This dual-modal delivery system effectively combines chemotherapy and immunotherapy, offering a promising solution for more effective cancer treatment and improved outcomes. Full article

(This article belongs to the Special Issue Combination Therapeutic Delivery Systems)

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20 pages, 10618 KiB

Open AccessArticle

Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers

by Shuailing Zhang, Hailin Yu, Bingquan Tian, Xiaoli Wang, Wenhao Cui, Lei Yang, Jingqian Li, Huihui Gong, Junsheng Zhao, Liqun Lu, Jing Zhao and Yubin Lan

Agronomy 2024, 14(10), 2205; https://doi.org/10.3390/agronomy14102205 (registering DOI) - 25 Sep 2024

Abstract

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned [...] Read more.

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R² is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. Full article

(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture—2nd Edition)

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19 pages, 352 KiB

Open AccessArticle

John Damascene’s Arguments about the Existence of God: A Logico-Philosophical and Religio-Hermeneutic Approach

by Vassilios Adrahtas

Religions 2024, 15(10), 1167; https://doi.org/10.3390/rel15101167 (registering DOI) - 25 Sep 2024

Abstract

The Exact Exposition of the Orthodox Faith is perhaps the most logically structured and inspired work not only in the oeuvre of the seventh-to-eighth-century theologian John Damascene, but most likely throughout the entire Greek Patristic literature. As such, the Exact Exposition definitely presents [...] Read more.

The Exact Exposition of the Orthodox Faith is perhaps the most logically structured and inspired work not only in the oeuvre of the seventh-to-eighth-century theologian John Damascene, but most likely throughout the entire Greek Patristic literature. As such, the Exact Exposition definitely presents some quite intriguing features, such as the prolific use of logical distinctions, syllogisms, or full-fledged arguments, to name a few. Regarding the latter, John Damascene’s use of certain arguments in order to prove the existence of God not only hold a unique place in Byzantine theology but have also exercised a tremendous influence on Eastern Orthodox apologetics. However, what I would call his rationalization agenda comes not only with merits but with faults as well. It is to both these that the present study draws attention by evaluating them logico-philosophically and interpreting them religio-hermeneutically. What is of special interest is the fact that John Damascene’s logical faults are the most interesting parts of his theologizing. Full article

(This article belongs to the Special Issue Patristics: Essays from Australia)

16 pages, 6757 KiB

Open AccessArticle

Transcriptomic Study of Different Stages of Development in the Testis of Sheep

by Binpeng Xi, Shengguo Zhao, Rui Zhang, Zengkui Lu, Jianye Li, Xuejiao An and Yaojing Yue

Animals 2024, 14(19), 2767; https://doi.org/10.3390/ani14192767 (registering DOI) - 25 Sep 2024

Abstract

Numerous genes govern male reproduction, modulating testicular development and spermatogenesis. Our study leveraged RNA-Seq to explore candidate genes and pivotal pathways influencing fecundity in an F1 hybrid of Southdown × Hu sheep testes across four developmental milestones: M0 (0 months old, newborn), M3 [...] Read more.

Numerous genes govern male reproduction, modulating testicular development and spermatogenesis. Our study leveraged RNA-Seq to explore candidate genes and pivotal pathways influencing fecundity in an F1 hybrid of Southdown × Hu sheep testes across four developmental milestones: M0 (0 months old, newborn), M3 (3 months old, sexually immature), M6 (6 months old, sexually mature), and Y1 (1 years old, adult). Histological examination using hematoxylins and eosin staining revealed that the cross-sectional area of the spermatid tubules and the number of supportive cells increased in the other groups, as compared to the M0 group. The cross-sectional area of the vasculature and the number of supporting cells were found to be significantly increased in all other groups in comparison to the M0 group. We conducted GO and KEGG analyses of the differentially expressed genes (DEGs) in the three comparison groups and identified key pathways, including cAMP, MAPK, ECM–receptor interactions, PI3K-Akt, and FOXO signaling, which are closely related to testicular development and spermatogenesis. Notably, alternative splicing (AS) events were markedly elevated in M6 and Y1 stages. Key genes like GATA4, GATA6, SMAD4, SOX9, YAP1, ITGB1 and MAPK1 emerged as significantly enriched in these pathways, potentially orchestrating the transition from immature to mature testes in sheep. These findings offer valuable insights into male reproductive potential and can inform strategies for optimizing animal breeding. Full article

(This article belongs to the Section Animal Reproduction)

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29 pages, 6252 KiB

Open AccessReview

Red Seaweed (Rhodophyta) Phycocolloids: A Road from the Species to the Industry Application

by Madalena Mendes, João Cotas, Diana Pacheco, Kay Ihle, Alina Hillinger, Miguel Cascais, João Carlos Marques, Leonel Pereira and Ana M. M. Gonçalves

Mar. Drugs 2024, 22(10), 432; https://doi.org/10.3390/md22100432 (registering DOI) - 25 Sep 2024

Abstract

Seaweed polysaccharides are versatile both in their functions in seaweed physiology and in their practical applications in society. However, their content and quality vary greatly. This review discusses the main factors that influence the yield and quality of polysaccharides, specifically carrageenans and agars [...] Read more.

Seaweed polysaccharides are versatile both in their functions in seaweed physiology and in their practical applications in society. However, their content and quality vary greatly. This review discusses the main factors that influence the yield and quality of polysaccharides, specifically carrageenans and agars (sulfated galactans) found in red algae species (Rhodophyta). In addition, its historical, current, and emerging applications are also discussed. Carrageenan has been influenced mainly by photosynthetically active radiation (PAR) and nitrogen, while its relationship with temperature has not yet been replicated by recent studies. Agar’s seasonal trend has also been found to be more ambiguous than stated before, with light, temperature, nutrients, and pH being influencing factors. In this review, it is also shown that, depending on the compound type, seaweed polysaccharides are influenced by very different key factors, which can be crucial in seaweed aquaculture to promote a high yield and quality of polysaccharides. Additionally, factors like the extraction method and storage of polysaccharides also influence the yield and quality of these compounds. This review also highlights the drawbacks and inadequacy inherent from the conventional (or current) extraction technology approaches. Full article

(This article belongs to the Special Issue Polysaccharides from Marine Environment)

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14 pages, 1576 KiB

Open AccessArticle

Language Model-Based Text Augmentation System for Cerebrovascular Disease Related Medical Report

by Yu-Hyeon Kim, Chulho Kim and Yu-Seop Kim

Appl. Sci. 2024, 14(19), 8652; https://doi.org/10.3390/app14198652 (registering DOI) - 25 Sep 2024

Abstract

Texts in medical fields containing sensitive information pose challenges for AI research usability. However, there is increasing interest in generating synthetic text to make medical text data bigger for text-based medical AI research. Therefore, this paper suggests a text augmentation system for cerebrovascular [...] Read more.

Texts in medical fields containing sensitive information pose challenges for AI research usability. However, there is increasing interest in generating synthetic text to make medical text data bigger for text-based medical AI research. Therefore, this paper suggests a text augmentation system for cerebrovascular diseases, using a synthetic text generation model based on DistilGPT2 and a classification model based on BioBERT. The synthetic text generation model generates synthetic text using randomly extracted reports (5000, 10,000, 15,000, and 20,000) from 73,671 reports. The classification model is fine-tuned with the entire report to annotate synthetic text and build a new dataset. Subsequently, we fine-tuned a classification model by incrementally increasing the amount of augmented data added to each original dataset. Experimental results show that fine-tuning by adding augmented data improves model performance by up to 20%. Furthermore, we found that generating a large amount of synthetic text is not necessarily required to achieve better performance, and the appropriate amount of data augmentation depends on the size of the original data. Therefore, our proposed method reduces the time and resources needed for dataset construction, automating the annotation task and generating meaningful synthetic text for medical AI research. Full article

(This article belongs to the Topic Innovation, Communication and Engineering)

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14 pages, 559 KiB

Open AccessReview

Synthesis and Application of Silver Nanoparticles for Caries Management: A Review

by Iris Xiaoxue Yin, Veena Wenqing Xu, Grace Yuchan Xu, Ollie Yiru Yu, John Yun Niu and Chun Hung Chu

Pharmaceuticals 2024, 17(10), 1264; https://doi.org/10.3390/ph17101264 (registering DOI) - 25 Sep 2024

Abstract

Silver nanoparticles have unique physical, chemical, and biological properties that make them attractive for medical applications. They have gained attention in dentistry for their potential use in caries management. This study reviews the different synthesis methods of silver nanoparticles and the application of [...] Read more.

Silver nanoparticles have unique physical, chemical, and biological properties that make them attractive for medical applications. They have gained attention in dentistry for their potential use in caries management. This study reviews the different synthesis methods of silver nanoparticles and the application of them for caries management. Silver nanoparticles are tiny silver and are typically less than 100 nanometres in size. They have a high surface area-to-volume ratio, making them highly reactive and allowing them to interact with bacteria and other materials at the molecular level. Silver nanoparticles have low toxicity and biocompatibility. Researchers have employed various methods to synthesise silver nanoparticles, including chemical, physical, and biological methods. By controlling the process, silver nanoparticles have defined sizes, shapes, and surface properties for wide use. Silver nanoparticles exhibit strong antibacterial properties, capable of inhibiting a broad range of bacteria, including antibiotic-resistant strains. They inhibit the growth of cariogenic bacteria, such as Streptococcus mutans. They can disrupt bacterial cell membranes, interfere with enzyme activity, and inhibit bacterial replication. Silver nanoparticles can inhibit biofilm formation, reducing the risk of caries development. Additionally, nano silver fluoride prevents dental caries by promoting tooth remineralisation. They can interact with the tooth structure and enhance the deposition of hydroxyapatite, aiding in repairing early-stage carious lesions. Silver nanoparticles can also be incorporated into dental restorative materials such as composite resins and glass ionomer cements. The incorporation can enhance the material’s antibacterial properties, reducing the risk of secondary caries and improving the longevity of the restoration. Full article

(This article belongs to the Special Issue Therapeutic Potential of Silver Nanoparticles (AgNPs))

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13 pages, 1997 KiB

Open AccessSystematic Review

The Antimicrobial Effect of the Incorporation of Inorganic Substances into Heat-Cured Denture Base Resins—A Systematic Review

by Mariana Lima, Helena Salgado, André Correia and Patrícia Fonseca

Prosthesis 2024, 6(5), 1189-1201; https://doi.org/10.3390/prosthesis6050085 (registering DOI) - 25 Sep 2024

Abstract

Introduction: Polymethylmethacrylate (PMMA) is the most widely used denture base material due to its favourable properties. Several studies have tested the incorporation of anti-infective agents into PMMA as a strategy to prevent biofilm growth on the denture surface. This systematic review aims to [...] Read more.

Introduction: Polymethylmethacrylate (PMMA) is the most widely used denture base material due to its favourable properties. Several studies have tested the incorporation of anti-infective agents into PMMA as a strategy to prevent biofilm growth on the denture surface. This systematic review aims to evaluate the efficacy of incorporating inorganic antimicrobial particles into denture base resins in preventing antimicrobial growth, thereby identifying the most effective agents for enhancing PMMA’s antimicrobial properties. Materials and methods: This systematic review followed the PRISMA guidelines, and the research protocol was registered in PROSPERO. The search was performed by using Medical Subject Headings and free text combined with Boolean operators in PubMed/Medline^® and in Cochrane^® and a free text combination in Web of Science^® Core Collection. Data regarding the inorganic particles studied, their antimicrobial effect, and the type of samples produced were collected and analysed. Results: After screening, a total of fifteen studies were included in this review. Most samples were disk-shaped and of varying sizes, and the most tested microbial strain was Candida albicans. Silver was the most used antimicrobial particle, followed by gold, titanium, and copper. Conclusions: Overall, incorporating inorganic particles into PMMA has produced promising antimicrobial results, depending on the concentration. Due to the high heterogeneity observed in the samples, more studies are recommended, particularly clinical trials. Full article

(This article belongs to the Special Issue Advancements in Prosthodontics: Exploring Innovations in Rehabilitation Medicine)

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