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27 pages, 1856 KiB  
Article
Remaining Useful Life Prediction for Rolling Bearings Based on TCN–Transformer Networks Using Vibration Signals
by Xiaochao Jin, Yaping Ji, Shiteng Li, Kailang Lv, Jianzheng Xu, Haonan Jiang and Shengnan Fu
Sensors 2025, 25(11), 3571; https://doi.org/10.3390/s25113571 - 5 Jun 2025
Abstract
Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning-based models based on multi-sensor monitoring data is fundamental for accurately [...] Read more.
Remaining useful life (RUL) prediction plays a core role in industrial prognostics and health management (PHM), requiring data-driven models with higher predictive capability for accurate long time series prediction. Developing reliable deep learning-based models based on multi-sensor monitoring data is fundamental for accurately predicting vibration trends during bearing operation and is crucial for bearing fault diagnosis and RUL prediction. In this work, a method for constructing a health index based on vibration signal is developed to describe the performance features of rolling bearings, which mainly includes feature extraction, sensitive feature index selection, dimensionality reduction, and normalization methods. In addition, a new RUL prediction method, TCN–Transformer, is developed which can efficiently learn and integrate local and global features of vibration signals, addressing the long time series prediction problem in RUL prediction. The TCN extracts local features, while the Transformer learns global features, both of which are seamlessly integrated through a specially designed feature fusion attention module. Both the health indicator (HI) constructed from extracted time domain and frequency domain feature parameters and the RUL prediction method were rigorously validated using the IEEE PHM 2012 Data Challenge dataset for rolling bearing prognostics. By employing the proposed HI construction method, the average comprehensive bearing performance index, used to evaluate RUL prediction accuracy, is improved by 8.69% across the entire dataset compared to the original feature-based composite index. The proposed RUL prediction model can more accurately predict the RUL of rolling bearings under different conditions, reducing the RMSE and MAE by 14.62% and 9.26%, respectively, and improving the SCORE by 13.04%. These results underscore the efficacy and superiority of our approach in RUL prediction of rotating machinery across varying conditions. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
18 pages, 3095 KiB  
Article
Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
by Chenrui Huang, Chaomin Mu, Hui Zhou and Quanmin Xie
Appl. Sci. 2025, 15(11), 6369; https://doi.org/10.3390/app15116369 - 5 Jun 2025
Abstract
Based on Brillouin optical time-domain reflectometry (BOTDR) technology, this study integrates laboratory tensile tests and similarity simulation experiments to systematically investigate the relationship between overlying strata collapse and fiber strain during coal seam mining. An analytical expression was established to describe the correlation [...] Read more.
Based on Brillouin optical time-domain reflectometry (BOTDR) technology, this study integrates laboratory tensile tests and similarity simulation experiments to systematically investigate the relationship between overlying strata collapse and fiber strain during coal seam mining. An analytical expression was established to describe the correlation between overlying strata displacement and fiber strain. The horizontal fiber monitoring results indicate that fiber strain accurately captures the evolution of overlying strata collapse and exhibits strong agreement with actual displacement height. When the working face advanced to 115 m and 155 m, the rock strata primarily underwent stress adjustment with minimal failure. At 195 m, the collapse zone expanded significantly, resulting in a notable increase in fiber strain. By 240 m, severe roof failure occurred, forming a complete caving zone in the goaf. The fiber strain curve exhibited a characteristic “double convex peak” pattern, with peak positions closely corresponding to rock fracture locations, further validating the feasibility of fiber monitoring in coal seam mining. Vertical fiber monitoring clearly delineated the evolution of the “three-zone” structure (caving zone, fracture zone, and bending subsidence zone) in the overlying strata. The fiber strain underwent a staged transformation from compressive strain to tensile strain, followed by stable compaction. The “stepped” characteristics of the strain curve effectively represented the heights of the three zones, highlighting the progressive and synchronized nature of rock failure. These findings demonstrate that fiber strain effectively characterizes the collapse height and evolution of overlying strata, enabling precise identification of rock fracture locations. This research provides scientific insights and technical support for roof stability assessment and mine safety management in coal seam mining. Full article
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27 pages, 3526 KiB  
Article
Addressing Sensor Data Heterogeneity and Sample Imbalance: A Transformer-Based Approach for Battery Degradation Prediction in Electric Vehicles
by Bi Wu, Shi Qiu and Wenhe Liu
Sensors 2025, 25(11), 3564; https://doi.org/10.3390/s25113564 - 5 Jun 2025
Abstract
Battery health monitoring and remaining useful life (RUL) estimation for electric vehicles face two significant challenges: battery data heterogeneity and sample imbalance. This study presents a novel approach based on Transformer architecture to specifically address these issues. We utilized the NASA lithium-ion battery [...] Read more.
Battery health monitoring and remaining useful life (RUL) estimation for electric vehicles face two significant challenges: battery data heterogeneity and sample imbalance. This study presents a novel approach based on Transformer architecture to specifically address these issues. We utilized the NASA lithium-ion battery cycling dataset, which contains charge-discharge and impedance measurement data under various temperature conditions. To tackle data heterogeneity, we developed a multimodal feature fusion strategy that effectively integrates battery sensor data from different sources and formats, including time-series charge-discharge sensor data and spectral impedance sensor measurements. To mitigate sample imbalance, we implemented an adaptive resampling technique and hierarchical attention mechanism, enhancing the model’s ability to recognize rare degradation patterns. Our Transformer-based model captures long-term dependencies in the battery degradation process through its self-attention mechanism. Experimental results demonstrate that the proposed solution significantly improves battery degradation prediction accuracy, achieving a 21.3% increase in accuracy when processing heterogeneous data and a 24.5% improvement in prediction capability for imbalanced samples compared to traditional methods. Additionally, through case studies, we validate the applicability of this method in actual electric vehicle battery management systems, providing reliable data support for battery preventive maintenance and replacement decisions. The findings have important implications for enhancing the reliability and economic efficiency of electric vehicle battery management systems. Full article
(This article belongs to the Section Vehicular Sensing)
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18 pages, 8075 KiB  
Article
Kinetic Aspects of Chrysotile Asbestos Thermal Decomposition Process
by Robert Kusiorowski, Anna Gerle, Magdalena Kujawa and Andrea Bloise
Minerals 2025, 15(6), 609; https://doi.org/10.3390/min15060609 - 5 Jun 2025
Abstract
Growing requirements in the field of environmental protection and waste management result in the need to search for new and effective methods of recycling various types of waste. From the perspective of technical and natural sciences, the disposal of hazardous waste, which can [...] Read more.
Growing requirements in the field of environmental protection and waste management result in the need to search for new and effective methods of recycling various types of waste. From the perspective of technical and natural sciences, the disposal of hazardous waste, which can lead to environmental degradation, is of utmost importance. A particularly hazardous waste is asbestos, used until recently in many branches of the economy and industry. Despite the ban on the production and use of asbestos introduced in many countries, products containing it are still present in the environment and pose a real threat. This paper presents the results of research related to the process of asbestos neutralization, especially the chrysotile variety, by the thermal decomposition method. Changes in the mineralogical characteristics of asbestos waste were studied using the following methods: TG-DTA-EGA, XRD, SEM-EDS and XRF. The characteristics of the chrysotile asbestos sample were determined before and after thermal treatment at selected temperatures. The second part of the study focuses on the kinetic aspect of this process, where the chrysotile thermal decomposition process was measured by two techniques: ex situ and in situ. This study showed that the chrysotile structure collapsed at approximately 600–800 °C through dehydroxylation, and then the fibrous chrysotile asbestos was transformed into new mineral phases, such as forsterite and enstatite. The formation of forsterite was observed at temperatures below 1000 °C, while enstatite was created above this temperature. From the kinetic point of view, the chrysotile thermal decomposition process could be described by the Avrami–Erofeev model, and the calculated activation energy values were ~180 kJ mol−1 and ~220 kJ mol−1 for ex situ and in situ processes, respectively. The obtained results indicate that the thermal method can be successfully used to detoxify hazardous chrysotile asbestos fibers. Full article
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22 pages, 7359 KiB  
Article
Rolling Bearing Life Prediction Based on Improved Transformer Encoding Layer and Multi-Scale Convolution
by Zhuopeng Luo, Zhihai Wang, Xiaoqin Liu and Yingming Yang
Machines 2025, 13(6), 491; https://doi.org/10.3390/machines13060491 - 5 Jun 2025
Abstract
To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency domain features are extracted [...] Read more.
To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency domain features are extracted from the vibration data covering the entire lifespan of the rolling bearings and passed through the transformer encoder layer. A novel dual-layer self-attention mechanism network structure is proposed to capture global information on the lifecycle progression of rolling bearings. Next, to further extract local temporal features within the bearing’s life cycle, a multi-scale convolution module is proposed to reinforce the local information across the entire lifespan. This method fully exploits both the long-term trends and short-term dynamic variations in the health status of rolling bearings, effectively enhancing the accuracy of life predictions. Experimental results show that, even under conditions with interference features, the TransCN model outperforms mainstream advantage model in terms of prediction accuracy and generalizability. This approach offers a new solution for managing the fault risk of rotating machinery and reducing maintenance costs. Full article
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19 pages, 6988 KiB  
Article
Unraveling the Impact of Inter-Basin Water Transfer on Soil Salinity and Sodicity and the Crop Yield Response in the Karamay Irrigation District of China
by Wei Liu, Xinwei Yin, Meng Zhu, Jutao Zhang, Wen Liu, Yingqing Su, Naying Chai and Yuhui Chen
Agronomy 2025, 15(6), 1386; https://doi.org/10.3390/agronomy15061386 - 5 Jun 2025
Abstract
Large-scale inter-basin water transfer is an important means to alleviate the pressure on water resources in water shortage regions. However, the long-term impacts of inter-basin transfers on the regional water–salt balance and associated land productivity remain poorly understood, especially in salt-affected arid environments. [...] Read more.
Large-scale inter-basin water transfer is an important means to alleviate the pressure on water resources in water shortage regions. However, the long-term impacts of inter-basin transfers on the regional water–salt balance and associated land productivity remain poorly understood, especially in salt-affected arid environments. To fill this gap, the core objective of this study was to reveal the implications of inter-basin water transfer on soil salinity and sodicity and the crop yield response under different irrigation practices. We conducted a case study on the Karamay irrigation district (KID), an artificial oasis with a 30-year history of inter-basin water transfer in northwestern China, using trend and correlation analyses, water–salt balance analyses, and salt-controlled yield reduction functions as well as field comprehensive measurements over 1996–2023. The results indicate that soil salinity and sodicity profiles, overall, exhibited a clear vertical stratification under both the early and late crop growing stages, and the degree of the soil salinization was decreasing, and the area of non-saline land was increasing significantly from 1996 to 2023 in the KID. Owing to the lack of salt-washing water and the poor irrigation water quality, the water-saving irrigated farmland was in the slight salt-aggregating state in the topsoil layer, while the other soil layers were in the salt-expelling or salt-equilibrating state in the KID. The profile distribution and exchange fluxes of soil salinity and sodicity are mainly characterized by climate, irrigation, and groundwater dynamics, as well as the plant salt tolerance, soil properties, and agronomic management which also influence the soil salt accumulation. With the transformation of irrigation schemes from traditional flood irrigation to modern water-saving irrigation during 1996–2023, the impact of soil salinity on relative crop yields has been substantially reduced in the KID, especially for salt-sensitive crops. This revealed that optimizing the drainage facilities, precise field irrigation and fertilization measures, and rational crop selection and agronomic practices are vital for high-quality development in the KID. Capitalizing on these research findings, we would provide effective directives for maintaining the sustainability of agricultural development in other similar inter-basin water transfer zones in the world. Full article
(This article belongs to the Section Water Use and Irrigation)
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19 pages, 12347 KiB  
Article
Long-Term Physical and Chemical Stability and Energy Recovery Potential Assessment of a New Chelating Resin Used in Brine Treatment for Chlor-Alkali Plants
by Liliana Lazar, Loredana-Vasilica Postolache, Valeria Danilova, Dumitru Coman, Adrian Bele, Daniela Rusu, Mirela-Fernanda Zaltariov and Gabriela Lisa
Polymers 2025, 17(11), 1575; https://doi.org/10.3390/polym17111575 - 5 Jun 2025
Abstract
Brine purification is an important process unit in chlor-alkali industrial plants for the production of sodium hydroxide, chlorine, and hydrogen. The membrane cell process requires ultrapure brine, which is obtained through mechanical filtration, chemical precipitation and fine polishing, and ion exchange using polymer [...] Read more.
Brine purification is an important process unit in chlor-alkali industrial plants for the production of sodium hydroxide, chlorine, and hydrogen. The membrane cell process requires ultrapure brine, which is obtained through mechanical filtration, chemical precipitation and fine polishing, and ion exchange using polymer resins. Temperature variations can lead to the degradation of the exchange properties of these resins, primarily causing a decrease in their exchange capacity, which negatively impacts the efficiency of the brine purification. After multiple ion exchange regeneration cycles, significant quantities of spent resins may be generated. These must be managed in accordance with resource efficiency and hazardous waste management to ensure the sustainability of the industrial process. In this paper, a comparative study is conducted to characterize the long-term stability of a new commercial chelating resin used in the industrial electrolysis process. The spectroscopic methods of physicochemical characterization included: scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) and attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR). The thermal behavior of the polymer resins was evaluated using the following thermogravimetric methods: thermogravimetry (TG), derivative thermogravimetry (DTG), and differential thermal analysis (DTA), while the moisture behavior was studied using dynamic vapor sorption (DVS) analysis. To assess the energy potential, the polymer resins were analyzed to determine their calorific value and overall energy content. Full article
(This article belongs to the Special Issue Current and Future Trends in Thermosetting Resins)
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12 pages, 821 KiB  
Review
The Potential Use of Fibrin Sealants in Burn Wound Management: A Comprehensive Review of Experimental and Clinical Studies
by Christina Nikolaou, Maximos Frountzas, Emmanouil I. Kapetanakis, Dimitrios Stefanoudakis, Nikolaos A. Papadopulos, Stylianos Kykalos, Dimitrios Schizas and Dimitrios Iliopoulos
Eur. Burn J. 2025, 6(2), 32; https://doi.org/10.3390/ebj6020032 - 5 Jun 2025
Abstract
Fibrin sealants have been implemented in the management of burn wounds. They can be used either in combination with skin grafts for full-thickness burns or alone for treating superficial and deep dermal burns. The aim of this review was to provide critical insights [...] Read more.
Fibrin sealants have been implemented in the management of burn wounds. They can be used either in combination with skin grafts for full-thickness burns or alone for treating superficial and deep dermal burns. The aim of this review was to provide critical insights regarding the efficacy of fibrin sealants in enhancing wound healing, improving graft adherence, and reducing complications. Therefore, evidence from experimental models and clinical trials was synthesized, underscoring the transformative role of fibrin sealants in modern burn care. This comprehensive review includes recent evidence on the potential benefits of fibrin sealants in the management of superficial and deep dermal or full-thickness burn injuries. Clinical and experimental evidence underscores some benefits in utilizing fibrin sealants in the management of superficial and deep dermal burn injuries, or in combination with skin grafts in full-thickness burns. Furthermore, fibrin sealants diminish postoperative pain and facilitate quick recovery for daily activities; however, controversy regarding their cost still remains. This review concludes that fibrin sealants could serve as a safe and effective therapeutic option for burn wound management. The safety and efficacy of their utilization, along with their wide availability and easiness to use, could make them an alternative treatment choice when a specialized plastic surgery service is not available, or in the emergency setting across different healthcare systems. Full article
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32 pages, 2378 KiB  
Review
Pyrolysis Process, Reactors, Products, and Applications: A Review
by Prakhar Talwar, Mariana Alzate Agudelo and Sonil Nanda
Energies 2025, 18(11), 2979; https://doi.org/10.3390/en18112979 - 5 Jun 2025
Abstract
With the rapid growth of the global population, increasing per capita energy demands, and waste generation, the need for innovative strategies to mitigate greenhouse gas emissions and effective waste management has become paramount. Pyrolysis, a thermochemical conversion process, facilitates the transformation of diverse [...] Read more.
With the rapid growth of the global population, increasing per capita energy demands, and waste generation, the need for innovative strategies to mitigate greenhouse gas emissions and effective waste management has become paramount. Pyrolysis, a thermochemical conversion process, facilitates the transformation of diverse biomass feedstocks, including agricultural biomass, forestry waste, and other carbonaceous wastes, into valuable biofuels such as bio-oil, biochar, and producer gas. The article reviews the benefits of pyrolysis as an effective and scalable technique for biofuel production from waste biomass. The review describes the different types of pyrolysis processes, such as slow, intermediate, fast, and catalytic, focusing on the effects of process parameters like temperature, heating rate, and residence time on biofuel yields and properties. The review also highlights the configurations and operating principles of different reactors used for pyrolysis, such as fixed bed, fluidized bed, entrained flow, plasma system, and microwaves. The review examines the factors affecting reactor performance, including energy consumption and feedstock attributes while highlighting the necessity of optimizing these systems to improve sustainability and economic feasibility in pyrolysis processes. The diverse value-added applications of biochar, bio-oil, and producer gas obtained from biomass pyrolysis are also discussed. Full article
(This article belongs to the Collection Bio-Energy Reviews)
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17 pages, 602 KiB  
Systematic Review
Addressing the Shortage of GLP-1 RA and Dual GIP/GLP-1 RA-Based Therapies—A Systematic Review
by Velimir Altabas, Zrinka Orlović and Maja Baretić
Diabetology 2025, 6(6), 52; https://doi.org/10.3390/diabetology6060052 - 5 Jun 2025
Abstract
Introduction: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucose-dependent insulinotropic peptide/glucagon-like peptide-1 receptor agonists (GIP/GLP-1 RAs) have transformed disease management, particularly in diabetes and obesity. However, recent shortages have disrupted patient care. This review explores the current evidence regarding their direct impact [...] Read more.
Introduction: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucose-dependent insulinotropic peptide/glucagon-like peptide-1 receptor agonists (GIP/GLP-1 RAs) have transformed disease management, particularly in diabetes and obesity. However, recent shortages have disrupted patient care. This review explores the current evidence regarding their direct impact on patient populations and reviews the mitigation strategies recommended by relevant health organizations. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for studies published from the earliest available data to 10 January 2025, using these terms: “GLP-1 AND shortage”, “liraglutide AND shortage”, “dulaglutide AND shortage”, “semaglutide AND shortage”, “exenatide AND shortage”, and “tirzepatide AND shortage”. Eligible studies needed to report measurable outcomes like prescription counts, specific laboratory findings, or the proportion of a study population achieving a defined outcome related to the shortage. Only English-language clinical research was considered, while other manuscripts were not included. The risk of bias was assessed using the Critical Appraisal Skills Programme checklist. Study characteristics and findings were summarized in tables. Results: Out of 295 identified manuscripts, 85 works were retained for further screening. Consequently, 8 studies met the inclusion criteria, covering 1036 participants with type 2 diabetes and 573 treated for obesity. In addition, two studies reported prescription prevalence, and one examined prescription counts. Key findings included reduced prescription rates and shifts in treatment practices. No studies assessed impacts on cardiovascular, renal outcomes, or mortality. Discussion and Conclusions: Evidence on the health effects of these shortages is limited. Existing studies highlight disruptions in diabetes and obesity care, but broader impacts remain unclear. Preventing future shortages requires coordinated efforts among all stakeholders. Therefore, we advocate for ethical planning, sustainable production, and fair distribution strategies to mitigate long-term consequences. Full article
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12 pages, 1646 KiB  
Article
Estimation of the Relative Chlorophyll Content of Pear Leaves Based on Field Spectrometry in Alaer, Xinjiang
by Yufen Huang, Zhenqi Fan, Hongxin Wu, Ximeng Zhang and Yanlong Liu
Sensors 2025, 25(11), 3552; https://doi.org/10.3390/s25113552 - 5 Jun 2025
Abstract
Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in [...] Read more.
Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R2) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R2 value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees. Full article
(This article belongs to the Section Sensing and Imaging)
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21 pages, 1213 KiB  
Article
Private Forest Owner Typology Based on Post-Disturbance Behaviour in Slovenia
by Darja Stare, Zala Uhan, Matevž Triplat, Špela Ščap, Nike Krajnc and Špela Pezdevšek Malovrh
Forests 2025, 16(6), 949; https://doi.org/10.3390/f16060949 - 4 Jun 2025
Abstract
In recent years, Europe has experienced an unexpectedly high frequency of natural disturbances. Private forest owners (PFOs), who manage a significant proportion of European forests and have diverse objectives and approaches to forest management, play a crucial role in salvage logging. The aim [...] Read more.
In recent years, Europe has experienced an unexpectedly high frequency of natural disturbances. Private forest owners (PFOs), who manage a significant proportion of European forests and have diverse objectives and approaches to forest management, play a crucial role in salvage logging. The aim of this study was to (i) categorise PFOs based on their forest management objectives, experience with regular forest management, and responses to natural disturbances, and (ii) propose policy implications for particular PFO groups to improve salvage logging operations and meet legal requirements. A survey was conducted among a random sample of PFOs whose forests were affected by natural disturbances (n = 547). The survey data were analysed using K-means cluster analysis, and three groups of PFOs were identified. The outsourcing-oriented managers (32%) responded most strongly to natural disturbances, with 96.0% carrying out salvage logging. This highly co-operative group often relies on forest contractors and demonstrates the highest commitment to performing forest management activities among the three groups. The self-reliant managers (42%) also responded strongly (92.6% carried out salvage logging) and are characterised by a strong preference for performing the work themselves. The group of less active managers (26%) included the highest proportion of PFOs who did not conduct salvage logging (19.0%) and those with no previous forest management experience (12.0%). Despite these differences, common policy instruments based on smart regulation principles are proposed to promote efficient salvage logging. The results may contribute to the holistic transformation of forest policy and management in response to the current challenges posed by large-scale natural disturbances. Full article
(This article belongs to the Special Issue Forest Disturbance and Management)
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29 pages, 1302 KiB  
Review
Artificial Intelligence (AI) in Surface Water Management: A Comprehensive Review of Methods, Applications, and Challenges
by Jerome G. Gacu, Cris Edward F. Monjardin, Ronald Gabriel T. Mangulabnan, Gerald Christian E. Pugat and Jerose G. Solmerin
Water 2025, 17(11), 1707; https://doi.org/10.3390/w17111707 - 4 Jun 2025
Abstract
Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications [...] Read more.
Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically explores the integration of Artificial Intelligence (AI) in surface flow management, encompassing applications in streamflow forecasting, sediment transport, flood prediction, water quality monitoring, and infrastructure operations such as dam and irrigation control. Drawing from over two decades of interdisciplinary literature, this study synthesizes recent advances in machine learning (ML), deep learning (DL), the Internet of Things (IoT), remote sensing, and hybrid AI–physics models. Unlike earlier reviews focusing on single aspects, this paper presents a systems-level perspective that links AI technologies to their operational, ethical, and governance dimensions. It highlights key AI techniques—including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer models, and Reinforcement Learning—and discusses their strengths, limitations, and implementation challenges, particularly in data-scarce and climate-uncertain regions. Novel insights are provided on Explainable AI (XAI), algorithmic bias, cybersecurity risks, and institutional readiness, positioning this paper as a roadmap for equitable and resilient AI adoption. By combining methodological analysis, conceptual frameworks, and future directions, this review offers a comprehensive guide for researchers, engineers, and policy-makers navigating the next generation of intelligent surface flow management. Full article
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18 pages, 690 KiB  
Review
The Lung Microbiome and Its Impact on Obstructive Sleep Apnea: A Diagnostic Frontier
by Aliki Karkala, Serafeim-Chrysovalantis Kotoulas, Asterios Tzinas, Eleni Massa, Eleni Mouloudi, Foteini Gkakou and Athanasia Pataka
Diagnostics 2025, 15(11), 1431; https://doi.org/10.3390/diagnostics15111431 - 4 Jun 2025
Abstract
Obstructive sleep apnea (OSA), a prevalent disorder characterized by recurrent upper airway collapse, is increasingly recognized as a systemic inflammatory condition influenced by microbial dysregulation. Emerging evidence underscores the lung microbiome as a mediator in OSA pathophysiology, where dysbiotic shifts driven by intermittent [...] Read more.
Obstructive sleep apnea (OSA), a prevalent disorder characterized by recurrent upper airway collapse, is increasingly recognized as a systemic inflammatory condition influenced by microbial dysregulation. Emerging evidence underscores the lung microbiome as a mediator in OSA pathophysiology, where dysbiotic shifts driven by intermittent hypoxia, oxidative stress and mechanical airway trauma amplify inflammatory cascades and perpetuate respiratory instability. This review synthesizes current knowledge on the bidirectional interplay between OSA and lung microbial communities. It aims to highlight how hypoxia-induced alterations in microbial ecology disrupt immune homeostasis, while inflammation-driven mucosal injury fosters pathogenic colonization. Clinical correlations between specific taxa like Streptococcus and Prevotella, and disease severity, suggest microbial signatures as novel biomarkers for OSA progression and treatment response. Furthermore, oxidative stress markers and pro-inflammatory cytokines emerge as potential diagnostic tools that bridge microbial dysbiosis with sleep-related outcomes. However, challenges persist in sampling standardization of the low-biomass lower airways, as well as in causative mechanisms linking microbial dysbiosis to OSA pathophysiology. By integrating microbial ecology with precision sleep medicine, this paradigm shift promises to transform OSA management from mechanical stabilization to holistic ecosystem restoration. Full article
(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)
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21 pages, 14472 KiB  
Article
RGD-DETR: Road Garbage Detection Based on Improved RT-DETR
by Zexing Luo, Meiqin Che, Qian Shao, Guoqing Yang, Changyong Xu and Yeqin Shao
Electronics 2025, 14(11), 2292; https://doi.org/10.3390/electronics14112292 - 4 Jun 2025
Abstract
Rapid urbanization in China has led to an increase in the volume of daily road garbage, posing challenges to municipal sanitation. Automatic garbage collection is thus essential for sustainable management. This paper proposes an improved RT-DETR-based (Real-Time Detection Transformer) detection model, RGD-DETR, to [...] Read more.
Rapid urbanization in China has led to an increase in the volume of daily road garbage, posing challenges to municipal sanitation. Automatic garbage collection is thus essential for sustainable management. This paper proposes an improved RT-DETR-based (Real-Time Detection Transformer) detection model, RGD-DETR, to improve road garbage detection performance. Firstly, an improved feature pyramid module that leverages multi-scale feature fusion techniques to enhance feature extraction effectiveness is designed. Secondly, a state space model is introduced to accurately capture long-range dependencies between image pixels with its spatial modeling capability, thus obtaining high-quality feature representation. Thirdly, a Dynamic Sorting-aware Decoder is adopted to embed a dynamic scoring module and a query-sorting module in adjacent decoder layers, enabling the model to focus on high-confidence predictions. Finally, the classification- and localization-oriented loss and matching cost are introduced to improve target localization accuracy. The experimental results on the road garbage dataset show that the RGD-DETR model improves detection accuracy (mAP) by 1.8% compared with the original RT-DETR, performing well for small targets and in occlusion scenarios. Full article
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