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Keywords = stability estimate

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24 pages, 10190 KiB  
Article
MSMT-RTDETR: A Multi-Scale Model for Detecting Maize Tassels in UAV Images with Complex Field Backgrounds
by Zhenbin Zhu, Zhankai Gao, Jiajun Zhuang, Dongchen Huang, Guogang Huang, Hansheng Wang, Jiawei Pei, Jingjing Zheng and Changyu Liu
Agriculture 2025, 15(15), 1653; https://doi.org/10.3390/agriculture15151653 - 31 Jul 2025
Abstract
Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision [...] Read more.
Accurate detection of maize tassels plays a crucial role in yield estimation of maize in precision agriculture. Recently, UAV and deep learning technologies have been widely introduced in various applications of field monitoring. However, complex field backgrounds pose multiple challenges against the precision detection of maize tassels, including maize tassel multi-scale variations caused by varietal differences and growth stage variations, intra-class occlusion, and background interference. To achieve accurate maize tassel detection in UAV images under complex field backgrounds, this study proposes an MSMT-RTDETR detection model. The Faster-RPE Block is first designed to enhance multi-scale feature extraction while reducing model Params and FLOPs. To improve detection performance for multi-scale targets in complex field backgrounds, a Dynamic Cross-Scale Feature Fusion Module (Dy-CCFM) is constructed by upgrading the CCFM through dynamic sampling strategies and multi-branch architecture. Furthermore, the MPCC3 module is built via re-parameterization methods, and further strengthens cross-channel information extraction capability and model stability to deal with intra-class occlusion. Experimental results on the MTDC-UAV dataset demonstrate that the MSMT-RTDETR significantly outperforms the baseline in detecting maize tassels under complex field backgrounds, where a precision of 84.2% was achieved. Compared with Deformable DETR and YOLOv10m, improvements of 2.8% and 2.0% were achieved, respectively, in the mAP50 for UAV images. This study proposes an innovative solution for accurate maize tassel detection, establishing a reliable technical foundation for maize yield estimation. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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22 pages, 1642 KiB  
Article
Spatiotemporal Dynamics of a Predator–Prey Model with Harvest and Disease in Prey
by Jingen Yang, Zhong Zhao, Yingying Kong and Jing Xu
Mathematics 2025, 13(15), 2474; https://doi.org/10.3390/math13152474 (registering DOI) - 31 Jul 2025
Abstract
In this paper, we propose a diffusion-type predator–prey interaction model with harvest and disease in prey, and conduct stability analysis and pattern formation analysis on the model. For the temporal model, the asymptotic stability of each equilibrium is analyzed using the linear stability [...] Read more.
In this paper, we propose a diffusion-type predator–prey interaction model with harvest and disease in prey, and conduct stability analysis and pattern formation analysis on the model. For the temporal model, the asymptotic stability of each equilibrium is analyzed using the linear stability method, and the conditions for Hopf bifurcation to occur near the positive equilibrium are investigated. The simulation results indicate that an increase in infection force might disrupt the stability of the model, while an increase in harvesting intensity would make the model stable. For the spatiotemporal model, a priori estimate for the positive steady state is obtained for the non-existence of the non-constant positive solution using maximum principle and Harnack inequality. The Leray–Schauder degree theory is used to study the sufficient conditions for the existence of non-constant positive steady states of the model, and pattern formation are achieved through numerical simulations. This indicates that the movement of prey and predators plays an important role in pattern formation, and different diffusions of these species may play essentially different effects. Full article
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16 pages, 763 KiB  
Article
Estimation of Genetic Parameters for Body Weight and Its Stability in Huaxi Cows from Xinjiang Region
by Ye Feng, Wenjuan Zhao, Xubin Lu, Xue Gao, Qian Zhang, Bin Zhang, Bao Wang, Fagang Zhong, Mengli Han and Zhi Chen
Animals 2025, 15(15), 2248; https://doi.org/10.3390/ani15152248 (registering DOI) - 31 Jul 2025
Abstract
In this study, we analyzed data from 2992 cows to comprehensively evaluate the adult weight (WEI), a key growth and body-size indicator, in West China cattle, aiming to estimate the related phenotypic and genetic parameters. The analysis focused on four weight traits while [...] Read more.
In this study, we analyzed data from 2992 cows to comprehensively evaluate the adult weight (WEI), a key growth and body-size indicator, in West China cattle, aiming to estimate the related phenotypic and genetic parameters. The analysis focused on four weight traits while considering non-genetic factors such as parity, season, year, and birth weight. Data were processed and corrected using a MIXED procedure and a multi-trait animal model. Results showed that these non-genetic factors significantly affected the weight traits (p < 0.05), which had high heritability (0.25–0.39) (p < 0.01). WEI is crucial for improving the genetic traits of cattle in western China and provides innovative approaches for optimizing herd management, enhancing the efficiency of genetic selection, and boosting beef cattle productivity. Full article
(This article belongs to the Section Cattle)
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59 pages, 2417 KiB  
Review
A Critical Review on the Battery System Reliability of Drone Systems
by Tianren Zhao, Yanhui Zhang, Minghao Wang, Wei Feng, Shengxian Cao and Gong Wang
Drones 2025, 9(8), 539; https://doi.org/10.3390/drones9080539 (registering DOI) - 31 Jul 2025
Abstract
The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements [...] Read more.
The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article
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17 pages, 5247 KiB  
Article
An Intelligent Optimization-Based Secure Filter Design for State Estimation of Power Systems with Multiple Disturbances
by Yudong Xu, Wei Wang, Yong Liu, Xiaokai Meng, Yutong Chen and Zhixiang Liu
Electronics 2025, 14(15), 3059; https://doi.org/10.3390/electronics14153059 (registering DOI) - 31 Jul 2025
Abstract
To address multiple disturbance threats such as system anomalies and cyberattacks faced by power systems, an intelligent optimized secure filter method is developed in this paper for state estimation of power systems with the aid of the improved sparrow search algorithm–optimized unscented Kalman [...] Read more.
To address multiple disturbance threats such as system anomalies and cyberattacks faced by power systems, an intelligent optimized secure filter method is developed in this paper for state estimation of power systems with the aid of the improved sparrow search algorithm–optimized unscented Kalman filter (ISSA-UKF). Firstly, the problem of insufficient robustness in noise modeling and parameter selection of the conventional unscented Kalman filter (UKF) is analyzed. Secondly, an intelligent optimization method is adopted to adaptively update the UKF’s process and measurement noise covariances in real time, and an ISSA-UKF fusion framework is constructed to improve the estimation accuracy and system response capability. Thirdly, an adaptive weight function based on disturbance observation differences is provided to strengthen the stability of the algorithm in response to abnormal measurements at edge nodes and dynamic system changes. Finally, simulation analysis under a typical power system model shows that compared with the conventional UKF method, the developed ISSA-UKF algorithm demonstrates significant improvements in estimation accuracy, robustness, and dynamic response performance and can effectively cope with non-ideal disturbances that may occur in power systems. Full article
(This article belongs to the Section Systems & Control Engineering)
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18 pages, 2612 KiB  
Article
Pressure Response of Crystalline Fluoranthene Probed by Raman Spectroscopy
by Olga Karabinaki, Stylianos Papastylianos, Nayra Machín Padrón, Antonios Hatzidimitriou, Dimitrios Christofilos and John Arvanitidis
Crystals 2025, 15(8), 697; https://doi.org/10.3390/cryst15080697 (registering DOI) - 30 Jul 2025
Abstract
The pressure response and structural stability of fluoranthene crystals up to 8 GPa are investigated using Raman spectroscopy. The vast majority of the Raman peaks upshift with pressure, either sublinearly (intermolecular modes) or quasilinearly (intramolecular modes), reflecting the bond hardening upon volume contraction. [...] Read more.
The pressure response and structural stability of fluoranthene crystals up to 8 GPa are investigated using Raman spectroscopy. The vast majority of the Raman peaks upshift with pressure, either sublinearly (intermolecular modes) or quasilinearly (intramolecular modes), reflecting the bond hardening upon volume contraction. The frequency shifts, accompanied by intensity redistribution among the Raman peaks, are by far larger for the former than those for the latter vibrations, compatible with their nature: weak intermolecular van der Waals interactions and strong intramolecular covalent bonds. For pressures higher than 2 GPa, changes in the linear pressure coefficients of the Raman peak frequencies, mainly towards lower values, are observed. These are more pronounced for intermolecular and C–H stretching vibrations. For P > 4.7 GPa, the pressure coefficients are further reduced, while all the observed pressure-induced changes are fully reversible upon pressure release. These changes may be interpreted either as two structural transitions at ~2 and ~4.7 GPa or as a single, but sluggish, structural phase transition in the pressure range 2–4.7 GPa, featuring the reorientation and different stacking of the molecules. From the high-pressure Raman data in the low-pressure phase, a bulk modulus of ~7 GPa at ambient pressure is estimated for solid fluoranthene. Full article
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27 pages, 12164 KiB  
Article
Neural Network Adaptive Attitude Control of Full-States Quad Tiltrotor UAV
by Jiong He, Binwu Ren, Yousong Xu, Qijun Zhao, Siliang Du and Bo Wang
Aerospace 2025, 12(8), 684; https://doi.org/10.3390/aerospace12080684 - 30 Jul 2025
Abstract
The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics [...] Read more.
The control stability and accuracy of quad tiltrotor UAVs is improved when encountering external disturbances during automatic flight by an active disturbance rejection control (ADRC) parameter self-tuning control strategy based on a radial basis function (RBF) neural network. Firstly, a nonlinear flight dynamics model of the quad tiltrotor UAV is established based on the approach of component-based mechanistic modeling. Secondly, the effects of internal uncertainties and external disturbances on the model are eliminated, whilst the online adaptive parameter tuning problem for the nonlinear active disturbance rejection controller is addressed. The superior nonlinear function approximation capability of the RBF neural network is then utilized by taking both the control inputs computed by the controller and the system outputs of the quad tiltrotor model as neural network inputs to implement adaptive parameter adjustments for the Extended State Observer (ESO) component responsible for disturbance estimation and the Nonlinear State Error Feedback (NLSEF) control law of the active disturbance rejection controller. Finally, an adaptive attitude control system for the quad tiltrotor UAV is constructed, centered on the ADRC-RBF controller. Subsequently, the efficacy of the attitude control system is validated through simulation, encompassing a range of flight conditions. The simulation results demonstrate that the Integral of Absolute Error (IAE) of the pitch angle response controlled by the ADRC-RBF controller is reduced to 37.4° in comparison to the ADRC controller in the absence of external disturbance in the full-states mode state of the quad tiltrotor UAV, and the oscillation amplitude of the pitch angle response controlled by the ADRC-RBF controller is generally reduced by approximately 50% in comparison to the ADRC controller in the presence of external disturbance. In comparison with the conventional ADRC controller, the proposed ADRC-RBF controller demonstrates superior performance with regard to anti-disturbance capability, adaptability, and tracking accuracy. Full article
(This article belongs to the Section Aeronautics)
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18 pages, 3379 KiB  
Article
Research on Electric Vehicle Differential System Based on Vehicle State Parameter Estimation
by Huiqin Sun and Honghui Wang
Vehicles 2025, 7(3), 80; https://doi.org/10.3390/vehicles7030080 - 30 Jul 2025
Abstract
To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating [...] Read more.
To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article
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24 pages, 1508 KiB  
Article
Genomic Prediction of Adaptation in Common Bean (Phaseolus vulgaris L.) × Tepary Bean (P. acutifolius A. Gray) Hybrids
by Felipe López-Hernández, Diego F. Villanueva-Mejía, Adriana Patricia Tofiño-Rivera and Andrés J. Cortés
Int. J. Mol. Sci. 2025, 26(15), 7370; https://doi.org/10.3390/ijms26157370 - 30 Jul 2025
Abstract
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, [...] Read more.
Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article
(This article belongs to the Special Issue Plant Breeding and Genetics: New Findings and Perspectives)
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20 pages, 9169 KiB  
Article
Dynamic Mission Planning Framework for Collaborative Underwater Operations Using Behavior Trees
by Seunghyuk Choi and Jongdae Jung
J. Mar. Sci. Eng. 2025, 13(8), 1458; https://doi.org/10.3390/jmse13081458 - 30 Jul 2025
Viewed by 33
Abstract
This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each [...] Read more.
This paper presents a behavior tree-based control architecture for end-to-end mission planning of an autonomous underwater vehicle (AUV) collaborating with a moving mothership in dynamic marine environments. The framework is organized into three phases—prepare and launch, execute the mission, and retrieval and docking—each encapsulated in an independent sub-tree to enable modular error handling and seamless phase transitions. The AUV and mothership operate entirely underwater, with real-time docking to a moving platform. An extended Kalman filter (EKF) fuses data from inertial, pressure, and acoustic sensors for accurate navigation and state estimation. At the same time, obstacle avoidance leverages forward-looking sonar (FLS)-based potential field methods to react to unpredictable underwater hazards. The system is implemented on the robot operating system (ROS) and validated in the Stonefish physics engine simulator. Simulation results demonstrate reliable mission execution, successful dynamic docking under communication delays and sensor noise, and robust retrieval from injected faults, confirming the validity and stability of the proposed architecture. Full article
(This article belongs to the Special Issue Innovations in Underwater Robotic Software Systems)
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17 pages, 539 KiB  
Article
Non-Fragile H Asynchronous State Estimation for Delayed Markovian Jumping NNs with Stochastic Disturbance
by Lan Wang, Juping Tang, Qiang Li, Xianwei Yang and Haiyang Zhang
Mathematics 2025, 13(15), 2452; https://doi.org/10.3390/math13152452 - 30 Jul 2025
Viewed by 78
Abstract
This article focuses on tackling the non-fragile H asynchronous estimation problem for delayed Markovian jumping neural networks (NNs) featuring stochastic disturbance. To more accurately reflect real-world scenarios, external random disturbances with known statistical characteristics are incorporated. Through the integration of stochastic analysis [...] Read more.
This article focuses on tackling the non-fragile H asynchronous estimation problem for delayed Markovian jumping neural networks (NNs) featuring stochastic disturbance. To more accurately reflect real-world scenarios, external random disturbances with known statistical characteristics are incorporated. Through the integration of stochastic analysis theory and Lyapunov stability techniques, as well as several matrix constraints formulas, some sufficient and effective results are addressed. These criteria ensure that the considered NNs achieve anticipant H stability in line with an external disturbance mitigation level. Meanwhile, the expected estimator gains will be explicitly constructed by dealing with corresponding matrix constraints. To conclude, a numerical simulation example is offered to showcase workability and validity of the formulated estimation method. Full article
(This article belongs to the Special Issue Advanced Filtering and Control Methods for Stochastic Systems)
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21 pages, 1210 KiB  
Article
Fixed-Time Bearing-Only Formation Control Without a Global Coordinate Frame
by Hanqiao Huang, Mengwen Lu, Bo Zhang and Qian Wang
Electronics 2025, 14(15), 3021; https://doi.org/10.3390/electronics14153021 - 29 Jul 2025
Viewed by 102
Abstract
This work addresses distributed fixed-time bearing-only formation stabilization for multi-agent systems lacking shared orientation knowledge. Addressing the challenge of missing global coordinate alignment in multi-agent systems, this work introduces a novel distributed estimator ensuring almost globally fixed-time convergence of orientation estimates. Leveraging this [...] Read more.
This work addresses distributed fixed-time bearing-only formation stabilization for multi-agent systems lacking shared orientation knowledge. Addressing the challenge of missing global coordinate alignment in multi-agent systems, this work introduces a novel distributed estimator ensuring almost globally fixed-time convergence of orientation estimates. Leveraging this estimator, we develop a distributed bearing-only formation control law specifically designed for agents governed by double-integrator dynamics, guaranteeing fixed-time convergence. Comprehensive stability analysis proves the almost global fixed-time stability of the overall closed-loop system. Crucially, the proposed control strategy drives actual formation to achieve the desired geometric pattern with almost global exponential convergence within a fixed time bound. Rigorous numerical experiments corroborate the theoretical framework. Full article
(This article belongs to the Special Issue Research on Cooperative Control of Multi-agent Unmanned Systems)
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19 pages, 664 KiB  
Article
Advanced Global CO2 Emissions Forecasting: Enhancing Accuracy and Stability Across Diverse Regions
by Adham Alsharkawi, Emran Al-Sherqawi, Kamal Khandakji and Musa Al-Yaman
Sustainability 2025, 17(15), 6893; https://doi.org/10.3390/su17156893 - 29 Jul 2025
Viewed by 112
Abstract
This study introduces a robust global time-series forecasting model developed to estimate CO2 emissions across diverse regions worldwide. The model employs a deep learning architecture with multiple hidden layers, ensuring both high predictive accuracy and temporal stability. Our methodology integrates innovative training [...] Read more.
This study introduces a robust global time-series forecasting model developed to estimate CO2 emissions across diverse regions worldwide. The model employs a deep learning architecture with multiple hidden layers, ensuring both high predictive accuracy and temporal stability. Our methodology integrates innovative training strategies and advanced optimization techniques to effectively handle heterogeneous time-series data. Emphasis is placed on the critical role of accurate and stable forecasts in supporting evidence-based policy-making and promoting environmental sustainability. This work contributes to global efforts to monitor and mitigate climate change, in alignment with the United Nations Sustainable Development Goals (SDGs). Full article
(This article belongs to the Special Issue Effectiveness Evaluation of Sustainable Climate Policies)
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27 pages, 7785 KiB  
Article
Estimation of Potato Growth Parameters Under Limited Field Data Availability by Integrating Few-Shot Learning and Multi-Task Learning
by Sen Yang, Quan Feng, Faxu Guo and Wenwei Zhou
Agriculture 2025, 15(15), 1638; https://doi.org/10.3390/agriculture15151638 - 29 Jul 2025
Viewed by 174
Abstract
Leaf chlorophyll content (LCC), leaf area index (LAI), and above-ground biomass (AGB) are important growth parameters for characterizing potato growth and predicting yield. While deep learning has demonstrated remarkable advancements in estimating crop growth parameters, the limited availability of field data often compromises [...] Read more.
Leaf chlorophyll content (LCC), leaf area index (LAI), and above-ground biomass (AGB) are important growth parameters for characterizing potato growth and predicting yield. While deep learning has demonstrated remarkable advancements in estimating crop growth parameters, the limited availability of field data often compromises model accuracy and generalizability, impeding large-scale regional applications. This study proposes a novel deep learning model that integrates multi-task learning and few-shot learning to address the challenge of low data in growth parameter prediction. Two multi-task learning architectures, MTL-DCNN and MTL-MMOE, were designed based on deep convolutional neural networks (DCNNs) and multi-gate mixture-of-experts (MMOE) for the simultaneous estimation of LCC, LAI, and AGB from Sentinel-2 imagery. Building on this, a few-shot learning framework for growth prediction (FSLGP) was developed by integrating simulated spectral generation, model-agnostic meta-learning (MAML), and meta-transfer learning strategies, enabling accurate prediction of multiple growth parameters under limited data availability. The results demonstrated that the incorporation of calibrated simulated spectral data significantly improved the estimation accuracy of LCC, LAI, and AGB (R2 = 0.62~0.73). Under scenarios with limited field measurement data, the multi-task deep learning model based on few-shot learning outperformed traditional mixed inversion methods in predicting potato growth parameters (R2 = 0.69~0.73; rRMSE = 16.68%~28.13%). Among the two architectures, the MTL-MMOE model exhibited superior stability and robustness in multi-task learning. Independent spatiotemporal validation further confirmed the potential of MTL-MMOE in estimating LAI and AGB across different years and locations (R2 = 0.37~0.52). These results collectively demonstrated that the proposed FSLGP framework could achieve reliable estimation of crop growth parameters using only a very limited number of in-field samples (approximately 80 samples). This study can provide a valuable technical reference for monitoring and predicting growth parameters in other crops. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 2030 KiB  
Article
Restoring Balance: Probiotic Modulation of Microbiota, Metabolism, and Inflammation in SSRI-Induced Dysbiosis Using the SHIME® Model
by Marina Toscano de Oliveira, Fellipe Lopes de Oliveira, Mateus Kawata Salgaço, Victoria Mesa, Adilson Sartoratto, Kalil Duailibi, Breno Vilas Boas Raimundo, Williams Santos Ramos and Katia Sivieri
Pharmaceuticals 2025, 18(8), 1132; https://doi.org/10.3390/ph18081132 - 29 Jul 2025
Viewed by 265
Abstract
Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus [...] Read more.
Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article
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