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Search Results (1,087)

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Keywords = two-stage optimization approach

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30 pages, 1974 KiB  
Article
How Beautiful Memories Stay and Encourage Intention to Recommend the Destination: The Moderating Role of Coastal Destination Competitiveness
by Kristi Karla Arina, Diena Mutiara Lemy, Innocentius Bernarto, Ferdi Antonio and Indah Fatmawati
Tour. Hosp. 2025, 6(3), 144; https://doi.org/10.3390/tourhosp6030144 - 18 Jul 2025
Abstract
This study examines how memorable tourism experiences (MTEs) influence tourists’ intention to recommend coastal tourism destinations. Using a quantitative approach of PLS-SEM analysis and a disjoint two-stage approach, this study examines MTE as a higher-order construct (HOC) with its seven dimensions and the [...] Read more.
This study examines how memorable tourism experiences (MTEs) influence tourists’ intention to recommend coastal tourism destinations. Using a quantitative approach of PLS-SEM analysis and a disjoint two-stage approach, this study examines MTE as a higher-order construct (HOC) with its seven dimensions and the moderating role of coastal destination competitiveness (CDC) in structural relationships. Data were collected through purposive sampling from 339 tourists who had visited Likupang, one of the priority tourism destinations in Indonesia. The results show that MTE plays a crucial role in increasing perceived economic value (PEV) and place attachment (PLA), and it is directly related to the intention to recommend the destination (ITRD). In addition to the prominent mediation role of PEV, these findings reveal that the CDC can strengthen or weaken the influence of these factors on tourists’ intention to provide recommendations. Specifically, the CDC can strengthen PLA influence towards intention to recommend, whereas, in contrast, it weakens the PEV in driving these intentions. The findings of this study expand the horizon of managing coastal tourism with an understanding of tourist behavior, particularly through a focus on improving MTE from the dynamics of its seven dimensions in encouraging promotion through tourist recommendations while optimizing the natural competitiveness elements of Likupang. Full article
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27 pages, 3704 KiB  
Article
Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables
by Sajjad Nematzadeh and Vedat Esen
Appl. Sci. 2025, 15(14), 8005; https://doi.org/10.3390/app15148005 - 18 Jul 2025
Abstract
Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters [...] Read more.
Precisely predicting photovoltaic (PV) output is crucial for reliable grid integration; so far, most models rely on site-specific sensor data or treat large meteorological datasets as black boxes. This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. Starting from 27 local and plant-level variables recorded at 15 min resolution for a 1 MW array in Çanakkale region, Türkiye (1 August 2022–3 August 2024), we apply a three-stage feature-selection pipeline: (i) variance filtering, (ii) hierarchical correlation clustering with Ward linkage, and (iii) a meta-heuristic optimizer that maximizes a neural-network R2 while penalizing poor or redundant inputs. The resulting subset, dominated by apparent temperature and diffuse, direct, global-tilted, and terrestrial irradiance, reduces dimensionality without significantly degrading accuracy. Feature importance is then quantified through two complementary aspects: (a) tree-based permutation scores extracted from a set of ensemble models and (b) information gain computed over random feature combinations. Both views converge on shortwave, direct, and global-tilted irradiance as the primary drivers of active power. Using only the selected features, the best model attains an average R2 ≅ 0.91 on unseen data. By utilizing transparent feature-reduction techniques and explainable importance metrics, the proposed approach delivers compact, more generalized, and reliable PV forecasts that generalize to sites lacking embedded sensor networks, and it provides actionable insights for plant siting, sensor prioritization, and grid-operation strategies. Full article
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28 pages, 7545 KiB  
Article
Estimation of Rice Leaf Nitrogen Content Using UAV-Based Spectral–Texture Fusion Indices (STFIs) and Two-Stage Feature Selection
by Xiaopeng Zhang, Yating Hu, Xiaofeng Li, Ping Wang, Sike Guo, Lu Wang, Cuiyu Zhang and Xue Ge
Remote Sens. 2025, 17(14), 2499; https://doi.org/10.3390/rs17142499 - 18 Jul 2025
Abstract
Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation [...] Read more.
Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation that integrates both spectral and texture features extracted from UAV-based multispectral imagery through the development of novel Spectral–Texture Fusion Indices (STFIs). Field data were collected under nitrogen gradient treatments across three critical growth stages: heading, early filling, and late filling. A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). The models—RFECV-RF, RFECV-XGBoost, SFS-SVR, and SFS-DNN—were evaluated using four feature configurations. The SFS-DNN model with STFIs achieved the highest prediction accuracy (R2 = 0.874, RMSE = 2.621 mg/g). SHAP analysis revealed the significant contribution of STFIs to model predictions, underscoring the effectiveness of integrating spectral and texture information. The proposed STFI-based framework demonstrates strong generalization across phenological stages and offers a scalable, interpretable approach for UAV-based nitrogen monitoring in rice production systems. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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27 pages, 11254 KiB  
Article
Improved RRT-Based Obstacle-Avoidance Path Planning for Dual-Arm Robots in Complex Environments
by Jing Wang, Genliang Xiong, Bowen Dang, Jianli Chen, Jixian Zhang and Hui Xie
Machines 2025, 13(7), 621; https://doi.org/10.3390/machines13070621 - 18 Jul 2025
Abstract
To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a [...] Read more.
To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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22 pages, 6134 KiB  
Article
The Evaluation of Small-Scale Field Maize Transpiration Rate from UAV Thermal Infrared Images Using Improved Three-Temperature Model
by Xiaofei Yang, Zhitao Zhang, Qi Xu, Ning Dong, Xuqian Bai and Yanfu Liu
Plants 2025, 14(14), 2209; https://doi.org/10.3390/plants14142209 - 17 Jul 2025
Abstract
Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid [...] Read more.
Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (TC) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of TC. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (Tr-3TC), derived from TC data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with Tr-3TC. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H2O m−2 s−1 and 0.555 mmol H2O m−2 s−1 for the respective years, indicating strong interannual stability. Full article
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20 pages, 3233 KiB  
Article
A Two-Stage Optimization Framework for UAV Fleet Sizing and Task Allocation in Emergency Logistics Using the GWO and CBBA
by Yongchao Zhang, Wei Xu, Helin Ye and Zhuoyong Shi
Drones 2025, 9(7), 501; https://doi.org/10.3390/drones9070501 - 16 Jul 2025
Viewed by 88
Abstract
The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to [...] Read more.
The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems. Full article
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23 pages, 951 KiB  
Article
Multi-Objective Evolution and Swarm-Integrated Optimization of Manufacturing Processes in Simulation-Based Environments
by Panagiotis D. Paraschos, Georgios Papadopoulos and Dimitrios E. Koulouriotis
Machines 2025, 13(7), 611; https://doi.org/10.3390/machines13070611 - 16 Jul 2025
Viewed by 152
Abstract
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data [...] Read more.
This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article
(This article belongs to the Section Advanced Manufacturing)
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17 pages, 2482 KiB  
Article
Assessment of Milk Quality in Skopelos Goats Under Low- and High-Input Farming Systems
by Zoitsa Basdagianni, Ioannis-Emmanouil Stavropoulos, Georgios Manessis, Georgios Arsenos and Ioannis Bossis
Appl. Sci. 2025, 15(14), 7906; https://doi.org/10.3390/app15147906 - 15 Jul 2025
Viewed by 195
Abstract
This study investigated the effect of different farming systems and lactation stages on the physicochemical characteristics, somatic cell count (SCC), and total bacterial count (TBC) of milk from Skopelos goats. This study was conducted over two consecutive lactation periods on two commercial farms [...] Read more.
This study investigated the effect of different farming systems and lactation stages on the physicochemical characteristics, somatic cell count (SCC), and total bacterial count (TBC) of milk from Skopelos goats. This study was conducted over two consecutive lactation periods on two commercial farms in Greece, an extensive system on Skopelos Island and an intensive system in the Attica region, involving 237 goats of shared genetic background, thereby minimizing genetic variability and strengthening the validity of the comparisons between the production systems. Higher milk yields were observed in the extensive system (0.98 vs. 0.85 kg/day), while milk from this system also had a higher protein (3.57% vs. 3.47%; p < 0.001) and casein content (2.72% vs. 2.57%; p < 0.001), which are traits favorable for cheese production. Fat content peaked during mid-lactation (4.83%; p < 0.05) and remained unaffected by the farming system. Lactose declined from early (4.74%) to late lactation (4.42%; p < 0.001). Both SCC and TBC were significantly elevated in the extensive system (p < 0.001), possibly due to hand milking, environmental exposure, and less-controlled hygiene conditions. These findings highlight a trade-off between the nutritional advantages of extensive systems and challenges related to milk hygiene. A balanced approach, optimizing both quality and sustainability, is recommended. Full article
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15 pages, 1617 KiB  
Article
A Stochastic Optimization Model for Multi-Airport Flight Cooperative Scheduling Considering CvaR of Both Travel and Departure Time
by Wei Cong, Zheng Zhao, Ming Wei and Huan Liu
Aerospace 2025, 12(7), 631; https://doi.org/10.3390/aerospace12070631 - 14 Jul 2025
Viewed by 82
Abstract
By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at [...] Read more.
By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article
(This article belongs to the Special Issue Flight Performance and Planning for Sustainable Aviation)
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20 pages, 3710 KiB  
Article
An Accurate LiDAR-Inertial SLAM Based on Multi-Category Feature Extraction and Matching
by Nuo Li, Yiqing Yao, Xiaosu Xu, Shuai Zhou and Taihong Yang
Remote Sens. 2025, 17(14), 2425; https://doi.org/10.3390/rs17142425 - 12 Jul 2025
Viewed by 228
Abstract
Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity [...] Read more.
Light Detection and Ranging(LiDAR)-inertial simultaneous localization and mapping (SLAM) is a critical component in multi-sensor autonomous navigation systems, providing both accurate pose estimation and detailed environmental understanding. Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity to noise and sparsity, and the inclusion of redundant or low-quality feature correspondences. These weaknesses hinder their performance in complex or dynamic environments and fail to meet the reliability requirements of autonomous systems. To overcome these challenges, we propose a novel and accurate LiDAR-inertial SLAM framework with three major contributions. First, we employ a robust multi-category feature extraction method based on principal component analysis (PCA), which effectively filters out noisy and weakly structured points, ensuring stable feature representation. Second, to suppress outlier correspondences and enhance pose estimation reliability, we introduce a coarse-to-fine two-stage feature correspondence selection strategy that evaluates geometric consistency and structural contribution. Third, we develop an adaptive weighted pose estimation scheme that considers both distance and directional consistency, improving the robustness of feature matching under varying scene conditions. These components are jointly optimized within a sliding-window-based factor graph, integrating LiDAR feature factors, IMU pre-integration, and loop closure constraints. Extensive experiments on public datasets (KITTI, M2DGR) and a custom-collected dataset validate the proposed method’s effectiveness. Results show that our system consistently outperforms state-of-the-art approaches in accuracy and robustness, particularly in scenes with sparse structure, motion distortion, and dynamic interference, demonstrating its suitability for reliable real-world deployment. Full article
(This article belongs to the Special Issue LiDAR Technology for Autonomous Navigation and Mapping)
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21 pages, 4628 KiB  
Article
Design and Performance Evaluation of a Sub-6 GHz Multi-Port Coupled Antenna for 5G NR Mobile Applications
by Cheol Yoon, Yunsub Lee, Wonmo Seong and Woosu Kim
Appl. Sci. 2025, 15(14), 7804; https://doi.org/10.3390/app15147804 - 11 Jul 2025
Viewed by 149
Abstract
This paper describes a compact multi-port sub-6 GHz multiple-input multiple-output (MIMO) antenna system tailored for 5G NR mobile terminals operating in the n77 (3.3–4.2 GHz), n78 (3.3–3.8 GHz), and n79 (4.4–5.0 GHz) frequency bands. The proposed design leverages a shared coupling approach that [...] Read more.
This paper describes a compact multi-port sub-6 GHz multiple-input multiple-output (MIMO) antenna system tailored for 5G NR mobile terminals operating in the n77 (3.3–4.2 GHz), n78 (3.3–3.8 GHz), and n79 (4.4–5.0 GHz) frequency bands. The proposed design leverages a shared coupling approach that exploits the smartphone metal frame as the radiating element, facilitating efficient integration within the spatial constraints of modern mobile devices. A two-stage method is used to mitigate the mutual coupling and correlation issues typically encountered when designing compact MIMO configurations. Initially, a four-port structure is used to evaluate broadband impedance and spatial feasibility. Based on the observed limitations in terms of isolation and the envelope correlation coefficient (ECC), the final configuration was reconfigured as an optimized two-port layout with a refined coupling geometry and effective current path control. The fabricated two-port prototype exhibited a measured voltage standing wave ratio below 3:1 across the n78 band on both ports, with the isolation levels attaining –12.4 dB and ECCs below 0.12. The radiation efficiency exceeded −6 dB across the operational band, and the radiation patterns were stable at 3.3, 3.5, and 3.8 GHz, confirming that the system was appropriate for MIMO deployment. The antenna supports asymmetric per-port efficiency targets ranging from −4.5 to −10 dB. These are the realistic layout constraints of commercial smartphones. In summary, this study shows that a metal frame integrated two-port MIMO antenna enables wideband sub-6 GHz operation by meeting the key impedance and system-level performance requirements. Our method can be used to develop a scalable platform assisting future multi-band antenna integration in mass-market 5G smartphones. Full article
(This article belongs to the Special Issue Antennas for Next-Generation Electromagnetic Applications)
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22 pages, 7140 KiB  
Article
Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning
by Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara and Javier Tardáguila
Agriculture 2025, 15(14), 1495; https://doi.org/10.3390/agriculture15141495 - 11 Jul 2025
Viewed by 134
Abstract
Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried [...] Read more.
Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R2 = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R2 = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article
(This article belongs to the Section Digital Agriculture)
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30 pages, 878 KiB  
Article
Berth Efficiency Under Risk Conditions in Seaports Through Integrated DEA and AHP Analysis
by Deda Đelović, Marinko Aleksić, Oto Iker and Michail Chalaris
J. Mar. Sci. Eng. 2025, 13(7), 1324; https://doi.org/10.3390/jmse13071324 - 10 Jul 2025
Viewed by 179
Abstract
In the context of increasingly complex and dynamic maritime logistics, seaports serve as critical nodes for intermodal transport, energy distribution, and global trade. Ensuring the safe and uninterrupted operation of port infrastructure—particularly berths—is vital for maintaining supply chain resilience. This study explores the [...] Read more.
In the context of increasingly complex and dynamic maritime logistics, seaports serve as critical nodes for intermodal transport, energy distribution, and global trade. Ensuring the safe and uninterrupted operation of port infrastructure—particularly berths—is vital for maintaining supply chain resilience. This study explores the impact of multiple risk categories on berth efficiency in a seaport, aligning with the growing emphasis on maritime safety and risk-informed decision-making. A two-stage methodology is adopted. In the first phase, the DEA CCR input-oriented model is employed to assess the efficiency of selected berths considered as Decision Making Units (DMUs). In the second phase, the Analytical Hierarchy Process (AHP) is used to categorize and quantify the impact of four major risk classes—operational, technical, safety, and environmental—on berth efficiency. The results demonstrate that operational and safety risks contribute 63.91% of the composite weight in the AHP risk assessment hierarchy. These findings are highly relevant to contemporary efforts in maritime risk modeling, especially for individual ports and port systems with high berth utilization and vulnerability to system disruptions. The proposed integrated approach offers a scalable and replicable decision-support tool for port authorities, port operators, planners, and maritime safety stakeholders, enabling proactive risk mitigation, optimal utilization of available resources in a port, and improved berth performance. Its methodological design is appropriately suited to support further applications in port resilience frameworks and maritime safety strategies, being one of the bases for establishing collision avoidance strategies related to an individual port and/or port system, too. Full article
(This article belongs to the Special Issue Recent Advances in Maritime Safety and Ship Collision Avoidance)
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21 pages, 3739 KiB  
Article
A Novel Energy Control Digital Twin System with a Resource-Aware Optimal Forecasting Model Selection Scheme
by Jin-Woo Kwon, Anwar Rubab and Won-Tae Kim
Appl. Sci. 2025, 15(14), 7738; https://doi.org/10.3390/app15147738 - 10 Jul 2025
Viewed by 125
Abstract
As global energy demand intensifies across industrial, commercial, and residential domains, efficient and accurate energy management and control become crucial. Energy Digital Twins (EDTs), leveraging sensor measurement data and precise time-series forecasting models, offer promising monitoring, prediction, and optimization solutions for such services. [...] Read more.
As global energy demand intensifies across industrial, commercial, and residential domains, efficient and accurate energy management and control become crucial. Energy Digital Twins (EDTs), leveraging sensor measurement data and precise time-series forecasting models, offer promising monitoring, prediction, and optimization solutions for such services. Edge computing enables EDTs to deliver real-time management services placed closer to users. However, the existing energy management methodologies may fail to consider the limited resources of edge environments, which may cause service delays and reduced accuracy in management services. To solve this problem, we propose a novel energy control digital twin system with a resource-aware optimal forecasting mode selection scheme. The system dynamically selects optimal forecasting models by integrating statistical features of the input time series with available resources. It employs a two-stage approach: first, it identifies promising models through similarity detection in past time series; second, this initial recommendation is refined by considering the available computing resources to pinpoint the optimal forecasting model. This mechanism enhances adaptability and responsiveness in resource-constrained environments. Utilizing real-world LPG consumption data from 887 sensors, the proposed system achieves forecasting accuracy comparable to previous methods while reducing latency by up to 19 times in low-resource settings. Full article
(This article belongs to the Special Issue Digital Twin and IoT)
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17 pages, 269 KiB  
Review
Perioperative Chemo/Immunotherapies in Lung Cancer: A Critical Review on the Value of Perioperative Sequences
by Thoma’ Dario Clementi, Francesca Colonese, Stefania Canova, Maria Ida Abbate, Luca Sala, Francesco Petrella, Gabriele Giuseppe Pagliari and Diego Luigi Cortinovis
Curr. Oncol. 2025, 32(7), 397; https://doi.org/10.3390/curroncol32070397 - 10 Jul 2025
Viewed by 213
Abstract
Resectable non-small cell lung cancer (NSCLC) continues to pose significant challenges with high recurrence and mortality rates, despite traditional platinum-based chemotherapy yielding only an approximate 5% improvement in 5-year overall survival when administered preoperatively or postoperatively. In recent years, the integration of immune [...] Read more.
Resectable non-small cell lung cancer (NSCLC) continues to pose significant challenges with high recurrence and mortality rates, despite traditional platinum-based chemotherapy yielding only an approximate 5% improvement in 5-year overall survival when administered preoperatively or postoperatively. In recent years, the integration of immune checkpoint inhibitors (ICIs), such as nivolumab, durvalumab and pembrolizumab, with platinum-based regimens in the perioperative setting has emerged as a transformative strategy. Our comprehensive review, based on a systematic bibliographic search of PubMed, Google Scholar, EMBASE, Cochrane Library, and clinicaltrials.gov, targeting pivotal clinical trials from the past two decades, examines the impact of these neoadjuvant and adjuvant chemoimmunotherapy approaches on major pathological response rates and overall survival in early-stage NSCLC. Although these perioperative strategies represent a paradigm shift in treatment, promising durable responses are offset by persistent recurrence, emphasizing the necessity for optimized treatment sequencing, duration, and the identification of predictive biomarkers. Collectively, our findings underscore the critical role of the perioperative schema, particularly the neoadjuvant component, which enables the evaluation of novel biomarkers as surrogates for overall survival, in improving patient outcomes and delineating future research directions aimed at reducing mortality and enhancing the quality of life for patients with resectable NSCLC. Full article
(This article belongs to the Special Issue The Current Status of Lung Cancer Surgery)
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