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17 pages, 4672 KiB  
Article
Oscillation Mechanism of SRF-PLL in Wind Power Systems Under Voltage Sags and Improper Control Parameters
by Guoqing Wang, Zhiyong Dai, Qitao Sun, Shuaishuai Lv, Nana Lu and Jinke Ma
Electronics 2025, 14(15), 3100; https://doi.org/10.3390/electronics14153100 (registering DOI) - 3 Aug 2025
Abstract
The synchronous reference frame phase-locked loop (SRF-PLL) is widely employed for grid synchronization in wind farms. However, it may exhibit oscillations under voltage sags or improper parameter settings. These oscillations may compromise the secure integration of large-scale wind power. Therefore, mitigating the oscillations [...] Read more.
The synchronous reference frame phase-locked loop (SRF-PLL) is widely employed for grid synchronization in wind farms. However, it may exhibit oscillations under voltage sags or improper parameter settings. These oscillations may compromise the secure integration of large-scale wind power. Therefore, mitigating the oscillations of the SRF-PLL is crucial for ensuring stable and reliable operation. To this end, this paper investigates the underlying oscillation mechanism of the SRF-PLL from local and global perspectives. By taking into account the grid voltage and control parameters, it is revealed that oscillations of the SRF-PLL can be triggered by grid voltage sags and/or the improper control parameters. More specifically, from the local perspective, the SRF-PLL exhibits distinct qualitative behaviors around its stable equilibrium points under different grid voltage amplitudes. As a result, when grid voltage sags occur, the SRF-PLL may exhibit multiple oscillation modes and experience a prolonged transient response. Furthermore, from the global viewpoint, the large-signal analysis reveals that the SRF-PLL has infinitely many asymmetrical convergence regions. However, the sizes of these asymmetrical convergence regions shrink significantly under low grid voltage amplitude and/or small control parameters. In this case, even if the parameters in the small-signal model of the SRF-PLL are well-designed, a small disturbance can shift the operating point into other regions, resulting in undesirable oscillations and a sluggish dynamic response. The validity of the theoretical analysis is further supported by experimental verification. Full article
21 pages, 26631 KiB  
Technical Note
Induced Polarization Imaging: A Geophysical Tool for the Identification of Unmarked Graves
by Matthias Steiner and Adrián Flores Orozco
Remote Sens. 2025, 17(15), 2687; https://doi.org/10.3390/rs17152687 (registering DOI) - 3 Aug 2025
Abstract
The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging [...] Read more.
The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging as a non-invasive remote sensing technique specifically suited for detecting and characterizing unmarked graves. IP leverages changes in the electrical properties of soil and pore water, influenced by the accumulation of organic matter from decomposition processes. Measurements were conducted at an inactive cemetery using non-invasive textile electrodes to map a documented grave from the early 1990s, with a survey design optimized for high spatial resolution. The results reveal a distinct polarizable anomaly at a 0.75–1.0 m depth with phase shifts exceeding 12 mrad, attributed to organic carbon from wooden burial boxes, and a plume-shaped conductive anomaly indicating the migration of dissolved organic matter. While electrical conductivity alone yielded diffuse grave boundaries, the polarization response sharply delineated the grave, aligning with photographic documentation. These findings underscore the value of IP imaging as a non-invasive, data-driven approach for the accurate localization and characterization of graves. The methodology presented here offers a promising new tool for archaeological prospection and forensic search operations, expanding the geophysical toolkit available for remote sensing in culturally and legally sensitive contexts. Full article
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22 pages, 2988 KiB  
Article
Enhanced Cuckoo Search Optimization with Opposition-Based Learning for the Optimal Placement of Sensor Nodes and Enhanced Network Coverage in Wireless Sensor Networks
by Mandli Rami Reddy, M. L. Ravi Chandra and Ravilla Dilli
Appl. Sci. 2025, 15(15), 8575; https://doi.org/10.3390/app15158575 (registering DOI) - 1 Aug 2025
Viewed by 22
Abstract
Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of [...] Read more.
Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m2 area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article
24 pages, 29785 KiB  
Article
Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification
by Nana Jiang, Wenbo Zhao, Jiao Guo, Qiang Zhao and Jubo Zhu
Remote Sens. 2025, 17(15), 2663; https://doi.org/10.3390/rs17152663 (registering DOI) - 1 Aug 2025
Viewed by 50
Abstract
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based [...] Read more.
Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. Full article
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26 pages, 315 KiB  
Article
Development of a Multicultural Leadership Promotion Program for Youth in Thailand’s Three Southern Border Provinces
by Kasetchai Laeheem, Punya Tepsing and Khaled Hayisa-e
Youth 2025, 5(3), 82; https://doi.org/10.3390/youth5030082 (registering DOI) - 1 Aug 2025
Viewed by 29
Abstract
Thailand’s southern border provinces need youth-focused multicultural leadership programs integrating local religious–cultural elements, community involvement, and long-term evaluation to enhance social cohesion and sustainable development. This study aimed to develop and evaluate a program to foster multicultural leadership among youth in Thailand’s three [...] Read more.
Thailand’s southern border provinces need youth-focused multicultural leadership programs integrating local religious–cultural elements, community involvement, and long-term evaluation to enhance social cohesion and sustainable development. This study aimed to develop and evaluate a program to foster multicultural leadership among youth in Thailand’s three southern border provinces. The research was conducted in two phases. The first phase involved synthesizing key multicultural leadership characteristics, designing a structured program and assessing its relevance and coherence through expert evaluation. The second phase focused on empirical validation by implementing the program with 22 selected youth participants, employing repeated-measures analysis of variance to assess its effectiveness. Additionally, experts evaluated the program’s validity, appropriateness, cost-effectiveness, utility, and feasibility. The resulting program, “EARCA”, comprises five core components: Experiential Exposure, Active Exploration & Engagement, Reflective Thinking & Analysis, Concept Integration & Synthesis, and Application & Extension. Expert assessments confirmed its appropriateness at the highest level, with a consistency index ranging from 0.8 to 1.0. Statistical analyses demonstrated significant improvements in all dimensions of multicultural leadership among participants. Furthermore, the program was rated highly accurate, appropriate, cost-effective, practical, and feasible for real-world implementation. These findings offer valuable insights for policymakers and practitioners seeking to enhance multicultural leadership development through structured, evidence-based interventions. Full article
19 pages, 9155 KiB  
Article
Microstructure Evolution in Homogenization Heat Treatment of Inconel 718 Manufactured by Laser Powder Bed Fusion
by Fang Zhang, Yifu Shen and Haiou Yang
Metals 2025, 15(8), 859; https://doi.org/10.3390/met15080859 (registering DOI) - 31 Jul 2025
Viewed by 75
Abstract
This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain [...] Read more.
This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain boundaries aligned with the build direction. Laves phase dissolution demonstrates dual-stage kinetics: initial rapid dissolution (0–15 min) governed by bulk atomic diffusion, followed by interface reaction-controlled deceleration (15–60 min) after 1 h at 1150 °C. Complete dissolution of the Laves phase is achieved after 3.7 h at 1150 °C. Recrystallization initiates preferentially at serrated grain boundaries through boundary bulging mechanisms, driven by localized orientation gradients and stored energy differentials. Grain growth kinetics obey a fourth-power time dependence, confirming Ostwald ripening-controlled boundary migration via grain boundary diffusion. Such a study is expected to be helpful in understanding the microstructural development of L-PBF-built IN718 under heat treatments. Full article
(This article belongs to the Section Additive Manufacturing)
22 pages, 10557 KiB  
Article
The RF–Absolute Gradient Method for Localizing Wheat Moisture Content’s Abnormal Regions with 2D Microwave Scanning Detection
by Dong Dai, Zhenyu Wang, Hao Huang, Xu Mao, Yehong Liu, Hao Li and Du Chen
Agriculture 2025, 15(15), 1649; https://doi.org/10.3390/agriculture15151649 - 31 Jul 2025
Viewed by 151
Abstract
High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization [...] Read more.
High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R2 = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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21 pages, 3327 KiB  
Article
Numerical Analysis of Heat Transfer and Flow Characteristics in Porous Media During Phase-Change Process of Transpiration Cooling for Aerospace Thermal Management
by Junhyeon Bae, Jukyoung Shin and Tae Young Kim
Energies 2025, 18(15), 4070; https://doi.org/10.3390/en18154070 (registering DOI) - 31 Jul 2025
Viewed by 157
Abstract
Transpiration cooling that utilizes the phase change of a liquid coolant is recognized as an effective thermal protection technique for extreme environments. However, the introduction of phase change within the porous structure brings about challenges, such as vapor blockage, pressure fluctuations, and temperature [...] Read more.
Transpiration cooling that utilizes the phase change of a liquid coolant is recognized as an effective thermal protection technique for extreme environments. However, the introduction of phase change within the porous structure brings about challenges, such as vapor blockage, pressure fluctuations, and temperature inversion, which critically influence system reliability. This study conducts numerical analyses of coupled processes of heat transfer, flow, and phase change in transpiration cooling using a Two-Phase Mixture Model. The simulation incorporates a Local Thermal Non-Equilibrium approach to capture the distinct temperature fields of the solid and fluid phases, enabling accurate prediction of the thermal response within two-phase and single-phase regions. The results reveal that under low heat flux, dominant capillary action suppresses dry-out and expands the two-phase region. Conversely, high heat flux causes vaporization to overwhelm the capillary supply, forming a superheated vapor layer and constricting the two-phase zone. The analysis also explains a paradoxical pressure drop, where an initial increase in flow rate reduces pressure loss by suppressing the high-viscosity vapor phase. Furthermore, a local temperature inversion, where the fluid becomes hotter than the solid matrix, is identified and attributed to vapor counterflow and its subsequent condensation. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
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15 pages, 10795 KiB  
Article
DigiHortiRobot: An AI-Driven Digital Twin Architecture for Hydroponic Greenhouse Horticulture with Dual-Arm Robotic Automation
by Roemi Fernández, Eduardo Navas, Daniel Rodríguez-Nieto, Alain Antonio Rodríguez-González and Luis Emmi
Future Internet 2025, 17(8), 347; https://doi.org/10.3390/fi17080347 (registering DOI) - 31 Jul 2025
Viewed by 132
Abstract
The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, [...] Read more.
The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article
(This article belongs to the Special Issue Advances in Smart Environments and Digital Twin Technologies)
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12 pages, 558 KiB  
Review
The Challenge of Rebuilding Gaza’s Health System: A Narrative Review Towards Sustainability
by Eduardo Missoni and Kasturi Sen
Healthcare 2025, 13(15), 1860; https://doi.org/10.3390/healthcare13151860 - 30 Jul 2025
Viewed by 679
Abstract
Background: Since the election of Hamas in 2006, Gaza has endured eight major military conflicts, culminating in the ongoing 2023–2025 war, now surpassing 520 days. This protracted violence, compounded by a 17-year blockade, has resulted in the near-total collapse of Gaza’s health [...] Read more.
Background: Since the election of Hamas in 2006, Gaza has endured eight major military conflicts, culminating in the ongoing 2023–2025 war, now surpassing 520 days. This protracted violence, compounded by a 17-year blockade, has resulted in the near-total collapse of Gaza’s health system. Over 49,000 deaths, widespread displacement, and the destruction of more than 60% of health infrastructure have overwhelmed both local capacity and international humanitarian response. Objectives: This narrative review aims to examine and synthesize the current literature (October 2023–April 2025) on the health crisis in Gaza, with a specific focus on identifying key themes and knowledge gaps relevant to rebuilding a sustainable health system. The review also seeks to outline strategic pathways for recovery in the context of ongoing conflict and systemic deprivation. Methods: Given the urgency and limitations of empirical data from conflict zones, a narrative review approach was adopted. Fifty-two sources—including peer-reviewed articles, editorials, reports, and correspondence—were selected through targeted searches using Medline and Google Scholar. The analysis was framed within a public health and political economy perspective, also taking health system building blocks into consideration. Results: The reviewed literature emphasizes emergency needs: trauma care, infectious disease control, and supply chain restoration. Innovations such as mobile clinics and telemedicine offer interim solutions. Gaps include limited attention to mental health (including that of health workers), local governance, and sustainable planning frameworks. Conclusions: Sustainable reconstruction requires a durable ceasefire; international stewardship aligned with local ownership; and a phased, equity-driven strategy emphasizing primary care, mental health, trauma management, and community engagement. Full article
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28 pages, 4666 KiB  
Article
Unmanned Aerial Vehicle Path Planning Based on Sparrow-Enhanced African Vulture Optimization Algorithm
by Weixiang Zhu, Xinghong Kuang and Haobo Jiang
Appl. Sci. 2025, 15(15), 8461; https://doi.org/10.3390/app15158461 - 30 Jul 2025
Viewed by 91
Abstract
Drones can improve the efficiency of point-to-point logistics and distribution and reduce labor costs; however, the complex three-dimensional airspace environment poses significant challenges for flight paths. To address this demand, this paper proposes a hybrid algorithm that integrates the Sparrow Search Algorithm (SSA) [...] Read more.
Drones can improve the efficiency of point-to-point logistics and distribution and reduce labor costs; however, the complex three-dimensional airspace environment poses significant challenges for flight paths. To address this demand, this paper proposes a hybrid algorithm that integrates the Sparrow Search Algorithm (SSA) with the African Vulture Optimization Algorithm (AVOA). Firstly, the algorithm introduces Sobol sequences at the population initialization stage to optimize the initial population; then, we incorporate SSA’s discoverer and vigilant mechanisms to balance exploration and exploitation and enhance global exploration capabilities; finally, multi-guide differencing and dynamic rotation transformation strategies are introduced in the first exploitation phase to enhance the direction of local exploitation by fusing multiple pieces of information; the second exploitation phase achieved a dynamic balance between elite guidance and population diversity through adaptive weight adjustment and enhanced Lévy flight strategy. In this paper, a three-dimensional model is built under a variety of constraints, and SAVOA (Sparrow-Enhanced African Vulture Optimization Algorithm) is compared with a variety of popular algorithms in simulation experiments. SAVOA achieves the optimal path in all scenarios, verifying the efficiency and superiority of the algorithm in UAV logistics path planning. Full article
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23 pages, 783 KiB  
Article
An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing
by Min Cui and Yipeng Wang
Sensors 2025, 25(15), 4705; https://doi.org/10.3390/s25154705 (registering DOI) - 30 Jul 2025
Viewed by 142
Abstract
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow scheduling [...] Read more.
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow scheduling algorithms to find optimal or near-optimal task-to-VM allocation solutions that meet users’ specific QoS requirements still remains an open area of research. In this paper, we propose a hybrid QoS-aware workflow scheduling algorithm named HLWOA to address the problem of simultaneously minimizing the completion time and execution cost of workflow scheduling in cloud computing. First, the workflow scheduling problem in cloud computing is modeled as a multi-objective optimization problem. Then, based on the heterogeneous earliest finish time (HEFT) heuristic optimization algorithm, tasks are reverse topologically sorted and assigned to virtual machines with the earliest finish time to construct an initial workflow task scheduling sequence. Furthermore, an improved Whale Optimization Algorithm (WOA) based on Lévy flight is proposed. The output solution of HEFT is used as one of the initial population solutions in WOA to accelerate the convergence speed of the algorithm. Subsequently, a Lévy flight search strategy is introduced in the iterative optimization phase to avoid the algorithm falling into local optimal solutions. The proposed HLWOA is evaluated on the WorkflowSim platform using real-world scientific workflows (Cybershake and Montage) with different task scales (100 and 1000). Experimental results demonstrate that HLWOA outperforms HEFT, HEPGA, and standard WOA in both makespan and cost, with normalized fitness values consistently ranking first. Full article
(This article belongs to the Section Internet of Things)
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23 pages, 6014 KiB  
Article
Modeling Water Table Response in Apulia (Southern Italy) with Global and Local LSTM-Based Groundwater Forecasting
by Lorenzo Di Taranto, Antonio Fiorentino, Angelo Doglioni and Vincenzo Simeone
Water 2025, 17(15), 2268; https://doi.org/10.3390/w17152268 - 30 Jul 2025
Viewed by 213
Abstract
For effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the [...] Read more.
For effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the shallow porous aquifer in Southern Italy. This aquifer is recharged by local rainfall, which exhibits minimal variation across the catchment in terms of volume and temporal distribution. To gain a deeper understanding of the complex interactions between precipitation and groundwater levels within the aquifer, we used water level data from six wells. Although these wells were not directly correlated in terms of individual measurements, they were geographically located within the same shallow aquifer and exhibited a similar hydrogeological response. The trained model uses two variables, rainfall and groundwater levels, which are usually easily available. This approach allowed the model, during the training phase, to capture the general relationships and common dynamics present across the different time series of wells. This methodology was employed despite the geographical distinctions between the wells within the aquifer and the variable duration of their observed time series (ranging from 27 to 45 years). The results obtained were significant: the global model, trained with the simultaneous integration of data from all six wells, not only led to superior performance metrics but also highlighted its remarkable generalization capability in representing the hydrogeological system. Full article
(This article belongs to the Section Hydrogeology)
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20 pages, 19642 KiB  
Article
SIRI-MOGA-UNet: A Synergistic Framework for Subsurface Latent Damage Detection in ‘Korla’ Pears via Structured-Illumination Reflectance Imaging and Multi-Order Gated Attention
by Baishao Zhan, Jiawei Liao, Hailiang Zhang, Wei Luo, Shizhao Wang, Qiangqiang Zeng and Yongxian Lai
Spectrosc. J. 2025, 3(3), 22; https://doi.org/10.3390/spectroscj3030022 - 29 Jul 2025
Viewed by 133
Abstract
Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature [...] Read more.
Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m−1 spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article
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16 pages, 3786 KiB  
Review
Topical Oxygen Therapy (blue®m) for Post-Surgical Care Protocols to Promote Wound Healing in Periodontology and Dental Implants: A Case-Based Literature Review
by Cristian Scognamiglio, Alessandro Perucchi, Chalini Sundar, Tatiana Miranda Deliberador and Hamdan Alghamdi
Oral 2025, 5(3), 53; https://doi.org/10.3390/oral5030053 - 29 Jul 2025
Viewed by 350
Abstract
Background: Stable post-surgical wound healing surrounding teeth and dental implants is essential for achieving excellent clinical outcomes, both during the initial phases of treatment and over the long term. Objectives: This work follows the new emerging trend of case-based literature reviews. The aim [...] Read more.
Background: Stable post-surgical wound healing surrounding teeth and dental implants is essential for achieving excellent clinical outcomes, both during the initial phases of treatment and over the long term. Objectives: This work follows the new emerging trend of case-based literature reviews. The aim of this review includes providing clinical findings from case series that demonstrate the efficacy of using blue®m oxygen treatment to promote post-surgical wound healing in patients that underwent periodontal and dental implant surgeries. In addition, a systematic review of the literature aimed to answer the focused research question: “In periodontal and implant surgeries, what are the aftercare protocols used to maintain optimal wound healing?” Case Presentation: One clinical case report involved the presentation of complex periodontal surgery. The other two cases focused on advanced implant surgeries. All patients were treated post-surgically with the local application of an oxygen-based therapy (blue®m) gel. This therapy was further emphasized during the wound-healing phase by instructing patients to maintain thorough dental hygiene using toothpaste and mouthwash containing a similar oxygen-release formulation (blue®m). Patients achieved satisfactory treatment outcomes. Systematic Review: PubMed and EMBASE were used in order to search for relevant studies in the scientific literature published up until June 2025. Only human clinical studies that used a specific protocol in regard to aftercare wound healing after periodontal or dental implant surgeries were included. As a result, 27 clinical studies were included. The outcome data were categorized and summarized. Conclusions: The use of local oxygen-based therapy showed a positive effect as a conventionally used aftercare modality in maintaining optimal post-surgical wound healing, following periodontal and implant surgeries. Further clinical studies are needed. Full article
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