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33 pages, 872 KiB

Open AccessReview

Implications of Fertilisation on Soil Nematode Community Structure and Nematode-Mediated Nutrient Cycling

by Lilian Salisi Atira and Thomais Kakouli-Duarte

Crops 2025, 5(4), 50; https://doi.org/10.3390/crops5040050 (registering DOI) - 30 Jul 2025

Soil nematodes are essential components of the soil food web and are widely recognised as key bioindicators of soil health because of their sensitivity to environmental factors and disturbance. In agriculture, many studies have documented the effects of fertilisation on nematode communities and [...] Read more.

Soil nematodes are essential components of the soil food web and are widely recognised as key bioindicators of soil health because of their sensitivity to environmental factors and disturbance. In agriculture, many studies have documented the effects of fertilisation on nematode communities and explored their role in nutrient cycling. Despite this, a key gap in knowledge still exists regarding how fertilisation-induced changes in nematode communities modify their role in nutrient cycling. We reviewed the literature on the mechanisms by which nematodes contribute to nutrient cycling and on how organic, inorganic, and recycling-derived fertilisers (RDFs) impact nematode communities. The literature revealed that the type of organic matter and its C:N ratio are key factors shaping nematode communities in organically fertilised soils. In contrast, soil acidification and ammonium suppression have a greater influence in inorganically fertilised soils. The key sources of variability across studies include differences in the amount of fertiliser applied, the duration of the fertiliser use, management practices, and context-specific factors, all of which led to differences in how nematode communities respond to both fertilisation regimes. The influence of RDFs on nematode communities is largely determined by the fertiliser’s origin and its chemical composition. While fertilisation-induced changes in nematode communities affect their role in nutrient cycling, oversimplifying experiments makes it difficult to understand nematodes’ functions in these processes. The challenges and knowledge gaps for further research to understand the effects of fertilisation on soil nematodes and their impact on nutrient cycling have been highlighted in this review to inform sustainable agricultural practices. Full article

(This article belongs to the Topic Soil Health and Nutrient Management for Crop Productivity)

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14 pages, 958 KiB

Open AccessArticle

Adverse Childhood Experiences, Genetic Susceptibility, and the Risk of Osteoporosis: A Cohort Study

by Yanling Shu, Chao Tu, Yunyun Liu, Lulu Song, Youjie Wang and Mingyang Wu

Medicina 2025, 61(8), 1387; https://doi.org/10.3390/medicina61081387 (registering DOI) - 30 Jul 2025

Abstract

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims [...] Read more.

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

(This article belongs to the Special Issue Advances in Rheumatic and Musculoskeletal Diseases and Health Promotion)

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17 pages, 3324 KiB

Open AccessArticle

Optimizing the Bioprocesses of Bacteriocin Production in Lacticaseibacillus paracasei HD1.7 by the “Acetate Switch”: Novel Insights Into the Labor Division Between Energy Metabolism, Quorum Sensing, and Acetate

by Weige Yao, Rui Sun, Wen Zhang, Jie Kang, Zhenchao Wu, Liangyang Mao, Ying Yang, Shuo Li, Gang Song, Jingping Ge and Wenxiang Ping

Foods 2025, 14(15), 2691; https://doi.org/10.3390/foods14152691 (registering DOI) - 30 Jul 2025

Abstract

Acetate may act as a signaling molecule, regulating Paracin 1.7 production via quorum sensing (QS) in Lacticaseibacillus paracasei HD1.7. The “acetate switch” phenomenon requires mechanistic exploration to optimize Paracin 1.7 production. The “acetate switch” phenomenon delays with higher glucose levels (30 h, 36 [...] Read more.

Acetate may act as a signaling molecule, regulating Paracin 1.7 production via quorum sensing (QS) in Lacticaseibacillus paracasei HD1.7. The “acetate switch” phenomenon requires mechanistic exploration to optimize Paracin 1.7 production. The “acetate switch” phenomenon delays with higher glucose levels (30 h, 36 h, and 96 h). Before the occurrence of the “acetate switch”, the ATP content increases and peaks at the “acetate switch” point and the NAD⁺/NADH ratio decreases, indicating energy changes. Moreover, the QS genes used for the pre-regulation of bacteriocin, such as prcKR, comCDE, were highly expressed. After the “acetate switch”, the ATP content decreased and the QS genes for the post-regulation of bacteriocin were highly expressed, such as rggs234 and sigma70-1/70-2. The “acetate switch” could act as an energy switch, regulating bacterial growth and QS genes. Before and after the “acetate switch”, some metabolic pathways were significantly altered according to the transcriptomic analysis by HD1.7 and HD1.7-Δpta. In this study, acetate was used as an input signal to regulate the two-component system, significantly influencing the bacteriocin expression system. And this study clarifies the roles of acetate, energy, and quorum sensing in promoting Paracin 1.7 production, providing a theoretical basis for optimizing the bacteriocin fermentation process of HD1.7. Full article

(This article belongs to the Section Food Microbiology)

14 pages, 449 KiB

Open AccessArticle

Head-to-Head Comparison of Etest, MICRONAUT-AM EUCAST and Reference Broth Microdilution-Based CLSI Results for Candida kefyr Antifungal Susceptibility Testing: Implications for Detection of Reduced Susceptibility to Amphotericin B

by Mohammad Asadzadeh, Suhail Ahmad, Jacques F. Meis, Josie E. Parker and Wadha Alfouzan

J. Fungi 2025, 11(8), 570; https://doi.org/10.3390/jof11080570 (registering DOI) - 30 Jul 2025

Abstract

Invasive infections with rare yeasts are increasing worldwide and are associated with higher mortality rates due to their resistance to antifungal drugs. Accurate antifungal susceptibility testing (AFST) is crucial for proper management of rare yeast infections. We performed AFST of 74 Candida kefyr [...] Read more.

Invasive infections with rare yeasts are increasing worldwide and are associated with higher mortality rates due to their resistance to antifungal drugs. Accurate antifungal susceptibility testing (AFST) is crucial for proper management of rare yeast infections. We performed AFST of 74 Candida kefyr isolates by Etest, EUCAST-based MICRONAUT-AM assay (MCN-AM) and reference Clinical and Laboratory Standards Institute broth microdilution method (CLSI). Essential agreement (EA, ±1 two-fold dilution), categorical agreement (CA), major errors (MEs) and very-major errors (VmEs) were determined using epidemiological cut-off values of ≤1.0 µg/mL, ≤0.03 µg/mL, ≤0.5 µg/mL and ≤1 µg/mL, defining wild-type isolates for fluconazole, voriconazole, micafungin and amphotericin B (AMB), respectively. Results for AMB susceptibility were correlated with ERG2/ERG3 mutations and total-cell sterols. CA of ≥97% was recorded between any two methods while EA varied between 72 and 82%, 87 and 92%, and 49 and 76% for fluconazole, voriconazole and micafungin, respectively. For AMB, CAs between CLSI and Etest; CLSI and MCN-AM; MCN-AM and Etest were 95% (4 ME, 0 VmE), 96% (3 ME, 0 VmE) and 99%, respectively, while EA varied from 32% to 69%. Non-synonymous ERG2/ERG3 mutations and no ergosterol were found in seven of eight isolates of non-wild types for AMB by Etest. Our data show that Etest, CLSI and MCN-AM methods are suitable for AFST of C. kefyr for fluconazole, voriconazole and micafungin. Excellent CAs for AMB between Etest and MCN-AM with concordant sterol profiles but not with CLSI suggest that Etest is also an excellent alternative for the detection of C. kefyr isolates with reduced susceptibility to AMB. Full article

(This article belongs to the Special Issue Candidiasis: Changes and Challenges in Its Epidemiology, Pathogenesis, Diagnosis, Treatment and Prevention)

26 pages, 62045 KiB

Open AccessArticle

CML-RTDETR: A Lightweight Wheat Head Detection and Counting Algorithm Based on the Improved RT-DETR

by Yue Fang, Chenbo Yang, Chengyong Zhu, Hao Jiang, Jingmin Tu and Jie Li

Electronics 2025, 14(15), 3051; https://doi.org/10.3390/electronics14153051 (registering DOI) - 30 Jul 2025

Abstract

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with [...] Read more.

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the

{AP}_{50}

of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

(This article belongs to the Section Artificial Intelligence)

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31 pages, 17812 KiB

Open AccessArticle

Deep Learning-Based Source Localization with Interference Striation of a Towed Horizontal Line Array

by Zhengchao Huang, Yanfa Deng, Peng Qian, Zhenglin Li and Peng Xiao

Electronics 2025, 14(15), 3053; https://doi.org/10.3390/electronics14153053 (registering DOI) - 30 Jul 2025

Abstract

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of [...] Read more.

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone. Full article

(This article belongs to the Special Issue Low-Frequency Underwater Acoustic Signal Processing and Applications)

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17 pages, 920 KiB

Open AccessArticle

Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning

by Tsung-Jung Tsai, Kun-Hua Lee, Chu-Kuang Chou, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Devansh Gupta, Gargi Ghosh, Tao-Yuan Liu and Hsiang-Chen Wang

Bioengineering 2025, 12(8), 828; https://doi.org/10.3390/bioengineering12080828 (registering DOI) - 30 Jul 2025

Abstract

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision [...] Read more.

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)

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23 pages, 7739 KiB

Open AccessArticle

AGS-YOLO: An Efficient Underwater Small-Object Detection Network for Low-Resource Environments

by Weikai Sun, Xiaoqun Liu, Juan Hao, Qiyou Yao, Hailin Xi, Yuwen Wu and Zhaoye Xing

J. Mar. Sci. Eng. 2025, 13(8), 1465; https://doi.org/10.3390/jmse13081465 (registering DOI) - 30 Jul 2025

Abstract

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a [...] Read more.

Detecting underwater targets is crucial for ecological evaluation and the sustainable use of marine resources. To enhance environmental protection and optimize underwater resource utilization, this study proposes AGS-YOLO, an innovative underwater small-target detection model based on YOLO11. Firstly, this study proposes AMSA, a multi-scale attention module, and optimizes the C3k2 structure to improve the detection and precise localization of small targets. Secondly, a streamlined GSConv convolutional module is incorporated to minimize the parameter count and computational load while effectively retaining inter-channel dependencies. Finally, a novel and efficient cross-scale connected neck network is designed to achieve information complementarity and feature fusion among different scales, efficiently capturing multi-scale semantics while decreasing the complexity of the model. In contrast with the baseline model, the method proposed in this paper demonstrates notable benefits for use in underwater devices constrained by limited computational capabilities. The results demonstrate that AGS-YOLO significantly outperforms previous methods in terms of accuracy on the DUO underwater dataset, with mAP@0.5 improving by 1.3% and mAP@0.5:0.95 improving by 2.6% relative to those of the baseline YOLO11n model. In addition, the proposed model also shows excellent performance on the RUOD dataset, demonstrating its competent detection accuracy and reliable generalization. This study proposes innovative approaches and methodologies for underwater small-target detection, which have significant practical relevance. Full article

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology, 2nd Edition)

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18 pages, 2448 KiB

Open AccessArticle

Inverse Punicines: Isomers of Punicine and Their Application in LiAlO₂, Melilite and CaSiO₃ Separation

by Maximilian H. Fischer, Ali Zgheib, Iliass El Hraoui, Alena Schnickmann, Thomas Schirmer, Gunnar Jeschke and Andreas Schmidt

Separations 2025, 12(8), 202; https://doi.org/10.3390/separations12080202 (registering DOI) - 30 Jul 2025

Abstract

The transition to sustainable energy systems demands efficient recycling methods for critical raw materials like lithium. In this study, we present a new class of pH- and light-switchable flotation collectors based on isomeric derivatives of the natural product Punicine, termed inverse Punicines. [...] Read more.

The transition to sustainable energy systems demands efficient recycling methods for critical raw materials like lithium. In this study, we present a new class of pH- and light-switchable flotation collectors based on isomeric derivatives of the natural product Punicine, termed inverse Punicines. These amphoteric molecules were synthesized via a straightforward four-step route and structurally tuned for hydrophobization by alkylation. Their performance as collectors was evaluated in microflotation experiments of lithium aluminate (LiAlO₂) and silicate matrix minerals such as melilite and calcium silicate. Characterization techniques including ultraviolet-visible (UV-Vis), nuclear magnetic resonance (NMR) and electron spin resonance (ESR) spectroscopy as well as contact angle, zeta potential (ζ potential) and microflotation experiments revealed strong pH- and structure-dependent interactions with mineral surfaces. Notably, N-alkylated inverse Punicine derivatives showed high flotation yields for LiAlO₂ at pH of 11, with a derivative possessing a dodecyl group attached to the nitrogen as collector achieving up to 86% recovery (collector conc. 0.06 mmol/L). Preliminary separation tests showed Li upgrading from 5.27% to 6.95%. Radical formation and light-response behavior were confirmed by ESR and flotation tests under different illumination conditions. These results demonstrate the potential of inverse Punicines as tunable, sustainable flotation reagents for advanced lithium recycling from complex slag systems. Full article

(This article belongs to the Special Issue Application of Green Flotation Technology in Mineral Processing)

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18 pages, 548 KiB

Open AccessArticle

Timing of Intervals Between Utterances in Typically Developing Infants and Infants Later Diagnosed with Autism Spectrum Disorder

by Zahra Poursoroush, Gordon Ramsay, Ching-Chi Yang, Eugene H. Buder, Edina R. Bene, Pumpki Lei Su, Hyunjoo Yoo, Helen L. Long, Cheryl Klaiman, Moira L. Pileggi, Natalie Brane and D. Kimbrough Oller

Brain Sci. 2025, 15(8), 819; https://doi.org/10.3390/brainsci15080819 (registering DOI) - 30 Jul 2025

Abstract

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: [...] Read more.

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

(This article belongs to the Special Issue Biomarker Development in the Early Identification of Autism Spectrum Disorders)

13 pages, 3360 KiB

Open AccessReview

Technological Advances in Pre-Operative Planning

by Mikolaj R. Kowal, Mohammed Ibrahim, André L. Mihaljević, Philipp Kron and Peter Lodge

J. Clin. Med. 2025, 14(15), 5385; https://doi.org/10.3390/jcm14155385 (registering DOI) - 30 Jul 2025

Abstract

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary [...] Read more.

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

(This article belongs to the Special Issue Surgical Precision: The Impact of AI and Robotics in General Surgery)

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18 pages, 2189 KiB

Open AccessArticle

A Synergistic Role of Photosynthetic Bacteria and Fungal Community in Pollutant Removal in an Integrated Aquaculture Wastewater Bioremediation System

by Muhammad Naeem Ramzan, Ding Shen, Yingzhen Wei, Bilal Raza, Hongmei Yuan, Arslan Emmanuel, Zulqarnain Mushtaq and Zhongming Zheng

Biology 2025, 14(8), 959; https://doi.org/10.3390/biology14080959 (registering DOI) - 30 Jul 2025

Abstract

This study addresses the understanding of fungal diversity and their bioremediation roles in an integrated aquaculture wastewater bioremediation system, an area less explored compared to bacteria, viruses, and protozoa. Despite the rapid advancement and affordability of molecular tools, insights into fungal communities remain [...] Read more.

This study addresses the understanding of fungal diversity and their bioremediation roles in an integrated aquaculture wastewater bioremediation system, an area less explored compared to bacteria, viruses, and protozoa. Despite the rapid advancement and affordability of molecular tools, insights into fungal communities remain vague, and interpreting environmental studies in an ecologically meaningful manner continues to pose challenges. To bridge this knowledge gap, we developed an integrated aquaculture wastewater bioremediation system, incorporating photosynthetic bacteria, and utilizing internal transcribed spacer (ITS) sequencing to analyze fungal community composition. Our findings indicate that the fungal community in aquaculture wastewater is predominantly composed of the phyla Ascomycota and Chytridiomycota, with dominant genera including Aspergillus, Hortea, and Ciliphora. FUNGuild, a user-friendly trait and character database operating at the genus level, facilitated the ecological interpretation of fungal functional groups. The analysis revealed significant negative correlations between nutrient levels (COD_mn, NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, and PO₄⁻³-P) and specific fungal functional groups, including epiphytes, animal pathogens, dung saprotrophs, plant pathogens, and ectomycorrhizal fungi. The removal rate for the COD_mn, NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, and PO₄⁻³-P were 71.42, 91.37, 88.80, 87.20, and 91.72% respectively. This study highlights the potential role of fungal communities in bioremediation processes and provides a framework for further ecological interpretation in aquaculture wastewater treatment systems. Full article

(This article belongs to the Special Issue The Relationship Between Water Quality and Aquatic Organisms (2nd Edition))

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16 pages, 1434 KiB

Open AccessArticle

Machine Learning-Based Identification of Risk Factors for ICU Mortality in 8902 Critically Ill Patients with Pandemic Viral Infection

by Elisabeth Papiol, Ricard Ferrer, Juan C. Ruiz-Rodríguez, Emili Díaz, Rafael Zaragoza, Marcio Borges-Sa, Julen Berrueta, Josep Gómez, María Bodí, Susana Sancho, Borja Suberviola, Sandra Trefler and Alejandro Rodríguez

J. Clin. Med. 2025, 14(15), 5383; https://doi.org/10.3390/jcm14155383 (registering DOI) - 30 Jul 2025

Abstract

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may [...] Read more.

Background/Objectives: The SARS-CoV-2 and influenza A (H1N1)pdm09 pandemics have resulted in high numbers of ICU admissions, with high mortality. Identifying risk factors for ICU mortality at the time of admission can help optimize clinical decision making. However, the risk factors identified may differ, depending on the type of analysis used. Our aim is to compare the risk factors and performance of a linear model (multivariable logistic regression, GLM) with a non-linear model (random forest, RF) in a large national cohort. Methods: A retrospective analysis was performed on a multicenter database including 8902 critically ill patients with influenza A (H1N1)pdm09 or COVID-19 admitted to 184 Spanish ICUs. Demographic, clinical, laboratory, and microbiological data from the first 24 h were used. Prediction models were built using GLM and RF. The performance of the GLM was evaluated by area under the ROC curve (AUC), precision, sensitivity, and specificity, while the RF by out-of-bag (OOB) error and accuracy. In addition, in the RF, the im-portance of the variables in terms of accuracy reduction (AR) and Gini index reduction (GI) was determined. Results: Overall mortality in the ICU was 25.8%. Model performance was similar, with AUC = 76% for GLM, and AUC = 75.6% for RF. GLM identified 17 independent risk factors, while RF identified 19 for AR and 23 for GI. Thirteen variables were found to be important in both models. Laboratory variables such as procalcitonin, white blood cells, lactate, or D-dimer levels were not significant in GLM but were significant in RF. On the contrary, acute kidney injury and the presence of Acinetobacter spp. were important variables in the GLM but not in the RF. Conclusions: Although the performance of linear and non-linear models was similar, different risk factors were determined, depending on the model used. This alerts clinicians to the limitations and usefulness of studies limited to a single type of model. Full article

(This article belongs to the Special Issue Current Trends and Prospects of Critical Emergency Medicine)

29 pages, 3731 KiB

Open AccessArticle

An Automated Method for Identifying Voids and Severe Loosening in GPR Images

by Ze Chai, Zicheng Wang, Zeshan Xu, Ziyu Feng and Yafeng Zhao

J. Imaging 2025, 11(8), 255; https://doi.org/10.3390/jimaging11080255 (registering DOI) - 30 Jul 2025

Abstract

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity [...] Read more.

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity of internal waveforms, a set of discriminative features is constructed. Based on these features, we develop the FKS-GPR dataset, a high-quality, manually annotated GPR dataset collected from real road environments, covering diverse and complex background conditions. Compared to datasets based on simulations, FKS-GPR offers higher practical relevance. An improved ACF-YOLO network is then designed for automatic detection, and the experimental results show that the proposed method achieves superior accuracy and robustness, validating its effectiveness and engineering applicability. Full article

(This article belongs to the Section Image and Video Processing)

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18 pages, 7003 KiB

Open AccessArticle

Integrating UAV and USV for Elaboration of High-Resolution Coastal Elevation Models

by Isabel López, Luis Bañón and José I. Pagán

J. Mar. Sci. Eng. 2025, 13(8), 1464; https://doi.org/10.3390/jmse13081464 (registering DOI) - 30 Jul 2025

Abstract

Coastal erosion, exacerbated by climate change, poses a critical global threat to both the environment and human livelihoods. Acquiring accurate, high-resolution topo-bathymetric data is vital for understanding these dynamic environments, without underestimating the hydrodynamic and meteo-oceanographic conditions. However, traditional methods often present significant [...] Read more.

Coastal erosion, exacerbated by climate change, poses a critical global threat to both the environment and human livelihoods. Acquiring accurate, high-resolution topo-bathymetric data is vital for understanding these dynamic environments, without underestimating the hydrodynamic and meteo-oceanographic conditions. However, traditional methods often present significant challenges in achieving comprehensive, high-resolution topo-bathymetric coverage efficiently in shallow coastal zones, leading to a notable ”white ribbon” data gap. This study introduces a novel, integrated methodology combining unmanned aerial vehicles (UAVs) for terrestrial surveys, unmanned surface vehicles (USVs) for bathymetry, and the Global Navigation Satellite System (GNSS) for ground control and intertidal gap-filling. Through this technologically rigorous approach, a seamless Bathymetry-Topography Digital Surface Model for the Guardamar del Segura dune system (Spain) was successfully elaborated using a DJI Mini 2 UAV, Leica Zeno FLX100 GNSS, and Apache 3 USV. The method demonstrated a substantial time reduction of at least 50–75% for comparable high-resolution coverage, efficiently completing the 86.4 ha field campaign in approximately 4 h. This integrated approach offers an accessible and highly efficient solution for generating detailed coastal elevation models crucial for coastal management and research. Full article

(This article belongs to the Special Issue Monitoring Coastal Systems and Improving Climate Change Resilience)

15 pages, 1308 KiB

Open AccessArticle

The Role of Emotional Understanding in Academic Achievement: Exploring Developmental Paths in Secondary School

by Luísa Faria, Ana Costa and Vladimir Taksic

J. Intell. 2025, 13(8), 96; https://doi.org/10.3390/jintelligence13080096 (registering DOI) - 30 Jul 2025

Abstract

The role of emotional intelligence (EI) in the academic context has been steadily established, together with its impact on students’ academic achievement, well-being, and professional success. Therefore, this study examined the development of a key EI ability—emotional understanding—throughout secondary school and explored its [...] Read more.

The role of emotional intelligence (EI) in the academic context has been steadily established, together with its impact on students’ academic achievement, well-being, and professional success. Therefore, this study examined the development of a key EI ability—emotional understanding—throughout secondary school and explored its impact on students’ academic achievement (maternal language and mathematics) at the end of this cycle, using the Vocabulary of Emotions Test. A total of 222 students were followed over the entire 3-year secondary cycle, using a three-wave longitudinal design spanning from 10th to 12th grade. At the first wave, participants were aged between 14 and 18 years (M = 15.4; SD = 0.63), with 58.6% being female. Overall, the results of Latent Growth Curve modeling indicated that students’ emotional understanding increased over the secondary school cycle. While student’s gender predicted the emotional understanding change patterns throughout secondary school, student’s GPA in 10th grade did not. Moreover, the initial levels of ability-based emotional understanding predicted students’ achievement in maternal language at the end of the cycle. Our findings offer valuable insights into how EI skills can contribute to academic endeavors in late adolescence and will explore their impact on educational settings. Full article

(This article belongs to the Special Issue Cognitive, Emotional, and Social Skills in Students)

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54 pages, 7675 KiB

Open AccessReview

Hydrogel Network Architecture Design Space: Impact on Mechanical and Viscoelastic Properties

by Andres F. Roca-Arroyo, Jhonatan A. Gutierrez-Rivera, Logan D. Morton and David A. Castilla-Casadiego

Gels 2025, 11(8), 588; https://doi.org/10.3390/gels11080588 (registering DOI) - 30 Jul 2025

Abstract

This comprehensive review explores the expansive design space of network architectures and their significant impact on the mechanical and viscoelastic properties of hydrogel systems. By examining the intricate relationships between molecular structure, network connectivity, and resulting bulk properties, we provide critical insights into [...] Read more.

This comprehensive review explores the expansive design space of network architectures and their significant impact on the mechanical and viscoelastic properties of hydrogel systems. By examining the intricate relationships between molecular structure, network connectivity, and resulting bulk properties, we provide critical insights into rational design strategies for tailoring hydrogel mechanics for specific applications. Recent advances in sequence-defined crosslinkers, dynamic covalent chemistries, and biomimetic approaches have significantly expanded the toolbox for creating hydrogels with precisely controlled viscoelasticity, stiffness, and stress relaxation behavior—properties that are crucial for biomedical applications, particularly in tissue engineering and regenerative medicine. Full article

(This article belongs to the Special Issue State-of-the Art Gel Research in USA)

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19 pages, 2284 KiB

Open AccessArticle

An Improved Wind Power Forecasting Model Considering Peak Fluctuations

by Shengjie Yang, Jie Tang, Lun Ye, Jiangang Liu and Wenjun Zhao

Electronics 2025, 14(15), 3050; https://doi.org/10.3390/electronics14153050 (registering DOI) - 30 Jul 2025

Abstract

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the [...] Read more.

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the power curve undergoes abrupt changes. To address the poor fitting at peaks, a short-term wind power forecasting method based on the improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model’s ability to capture regional segment features along the temporal dimension, enhancing the model’s receptive field to address wind power fluctuation under varying environmental conditions. Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model’s ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results, this enables the model to simultaneously leverage neighboring temporal segment features and long-range inter-temporal dependencies for future wind-power prediction, thereby substantially improving the fitting accuracy at power-curve peaks. Experimental results validate the effectiveness and practicality of the proposed model; compared with other models, the proposed approach reduces MAE by 9.14–42.31% and RMSE by 12.57–47.59%. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications)

28 pages, 17610 KiB

Open AccessArticle

Histological Assessment of Intestinal Changes Induced by Liquid Whey-Enriched Diets in Pigs

by Kamel Mhalhel, Mauro Cavallaro, Lidia Pansera, Leyanis Herrera Ledesma, Maria Levanti, Antonino Germanà, Anna Maria Sutera, Giuseppe Tardiolo, Alessandro Zumbo, Marialuisa Aragona and Giuseppe Montalbano

Vet. Sci. 2025, 12(8), 716; https://doi.org/10.3390/vetsci12080716 (registering DOI) - 30 Jul 2025

Abstract

Liquid whey (LW) is a nutrient-rich dairy by-product and a promising resource for animal nutrition. However, data regarding its impact on intestinal morphology and endocrine signaling are limited. Therefore, the current study aims to dissect those aspects. An experiment was conducted on 14 [...] Read more.

Liquid whey (LW) is a nutrient-rich dairy by-product and a promising resource for animal nutrition. However, data regarding its impact on intestinal morphology and endocrine signaling are limited. Therefore, the current study aims to dissect those aspects. An experiment was conducted on 14 crossbred pigs divided into control (fed 3% of their body weight pelleted feed) and LW (fed 3% of their body weight supplemented with 1.5 L of LW) groups. The results show a significantly increased body weight gain in LW pigs during the second half of the experiment. Moreover, an increased ileal villus height, deeper crypts, and a thicker muscularis externa in the duodenum and jejunum have been reported in LW-fed pigs. Goblet cell count revealed a significant abundance of these cells in duodenal villi and jejunal crypts of the LW group, suggesting enhanced mucosal defense in all segments of LW-fed pigs. While Cholecystokinin8 and Galanin showed the same expression pattern among both groups and SI segments, the leptin expression was significantly higher in LW swine. These findings indicate that LW promotes growth, gut mucosa remodeling, and neuroendocrine signaling, thus supporting LW use as a functional dietary strategy with attention to the adaptation period. Full article

(This article belongs to the Section Anatomy, Histology and Pathology)

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25 pages, 2693 KiB

Open AccessArticle

Adipokine and Hepatokines in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): Current and Developing Trends

by Salvatore Pezzino, Stefano Puleo, Tonia Luca, Mariacarla Castorina and Sergio Castorina

Biomedicines 2025, 13(8), 1854; https://doi.org/10.3390/biomedicines13081854 (registering DOI) - 30 Jul 2025

Abstract

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health challenge characterized by complex adipose–liver interactions mediated by adipokines and hepatokines. Despite rapid field evolution, a comprehensive understanding of research trends and translational advances remains fragmented. This study systematically maps the [...] Read more.

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health challenge characterized by complex adipose–liver interactions mediated by adipokines and hepatokines. Despite rapid field evolution, a comprehensive understanding of research trends and translational advances remains fragmented. This study systematically maps the scientific landscape through bibliometric analysis, identifying emerging domains and future clinical translation directions. Methods: A comprehensive bibliometric analysis of 1002 publications from 2004 to 2025 was performed using thematic mapping, temporal trend evaluation, and network analysis. Analysis included geographical and institutional distributions, thematic cluster identification, and research paradigm evolution assessment, focusing specifically on adipokine–hepatokine signaling mechanisms and clinical implications. Results: The United States and China are at the forefront of research output, whereas European institutions significantly contribute to mechanistic discoveries. The thematic map analysis reveals the motor/basic themes residing at the heart of the field, such as insulin resistance, fatty liver, metabolic syndrome, steatosis, fetuin-A, and other related factors that drive innovation. Basic clusters include metabolic foundations (obesity, adipose tissue, FGF21) and adipokine-centered subjects (adiponectin, leptin, NASH). New themes focus on inflammation, oxidative stress, gut microbiota, lipid metabolism, and hepatic stellate cells. Niche areas show targeted fronts such as exercise therapies, pediatric/novel adipokines (chemerin, vaspin, omentin-1), and advanced molecular processes that focus on AMPK and endoplasmic-reticulum stress. Temporal analysis shows a shift from single liver studies to whole models that include the gut microbiota, mitochondrial dysfunction, and interactions between other metabolic systems. The network analysis identifies nine major clusters: cardiovascular–metabolic links, adipokine–inflammatory pathways, hepatokine control, and new therapeutic domains such as microbiome interventions and cellular stress responses. Conclusions: In summary, this study delineates current trends and emerging areas within the field and elucidates connections between mechanistic research and clinical translation to provide guidance for future research and development in this rapidly evolving area. Full article

(This article belongs to the Special Issue Advances in Hepatology)

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26 pages, 1494 KiB

Open AccessArticle

Human–Robot Interaction and Tracking System Based on Mixed Reality Disassembly Tasks

by Raúl Calderón-Sesmero, Adrián Lozano-Hernández, Fernando Frontela-Encinas, Guillermo Cabezas-López and Mireya De-Diego-Moro

Robotics 2025, 14(8), 106; https://doi.org/10.3390/robotics14080106 (registering DOI) - 30 Jul 2025

Abstract

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen [...] Read more.

Disassembly is a crucial process in industrial operations, especially in tasks requiring high precision and strict safety standards when handling components with collaborative robots. However, traditional methods often rely on rigid and sequential task planning, which makes it difficult to adapt to unforeseen changes or dynamic environments. This rigidity not only limits flexibility but also leads to prolonged execution times, as operators must follow predefined steps that do not allow for real-time adjustments. Although techniques like teleoperation have attempted to address these limitations, they often hinder direct human–robot collaboration within the same workspace, reducing effectiveness in dynamic environments. In response to these challenges, this research introduces an advanced human–robot interaction (HRI) system leveraging a mixed-reality (MR) interface embedded in a head-mounted device (HMD). The system enables operators to issue real-time control commands using multimodal inputs, including voice, gestures, and gaze tracking. These inputs are synchronized and processed via the Robot Operating System (ROS2), enabling dynamic and flexible task execution. Additionally, the integration of deep learning algorithms ensures precise detection and validation of disassembly components, enhancing accuracy. Experimental evaluations demonstrate significant improvements, including reduced task completion times, enhanced operator experience, and compliance with strict adherence to safety standards. This scalable solution offers broad applicability for general-purpose disassembly tasks, making it well-suited for complex industrial scenarios. Full article

(This article belongs to the Special Issue Robot Teleoperation Integrating with Augmented Reality)

17 pages, 1460 KiB

Open AccessArticle

Rhizobacteria’s Effects on the Growth and Competitiveness of Solidago canadensis Under Nutrient Limitation

by Zhi-Yun Huang, Ying Li, Hu-Anhe Xiong, Misbah Naz, Meng-Ting Yan, Rui-Ke Zhang, Jun-Zhen Liu, Xi-Tong Ren, Guang-Qian Ren, Zhi-Cong Dai and Dao-Lin Du

Agriculture 2025, 15(15), 1646; https://doi.org/10.3390/agriculture15151646 (registering DOI) - 30 Jul 2025

Abstract

The role of rhizosphere bacteria in facilitating plant invasion is increasingly acknowledged, yet the influence of specific microbial functional traits remains insufficiently understood. This study addresses this gap by isolating two bacterial strains, Bacillus sp. ScRB44 and Pseudomonas sp. ScRB22, from the rhizosphere [...] Read more.

The role of rhizosphere bacteria in facilitating plant invasion is increasingly acknowledged, yet the influence of specific microbial functional traits remains insufficiently understood. This study addresses this gap by isolating two bacterial strains, Bacillus sp. ScRB44 and Pseudomonas sp. ScRB22, from the rhizosphere of the invasive weed Solidago canadensis. We assessed their nitrogen utilization capacity and indoleacetic acid (IAA) production capabilities to evaluate their ecological functions. Our three-stage experimental design encompassed strain promotion, nutrient stress, and competition phases. Bacillus sp. ScRB44 demonstrated robust IAA production and significantly improved the nitrogen utilization efficiency, significantly enhancing S. canadensis growth, especially under nutrient-poor conditions, and promoting a shift in biomass allocation toward the roots, thereby conferring a competitive advantage over native species. Conversely, Pseudomonas sp. ScRB22 exhibited limited functional activity and a negligible impact on plant performance. These findings underscore that the ecological impact of rhizosphere bacteria on invasive weeds is closely linked to their specific growth-promoting functions. By enhancing stress adaptation and optimizing resource allocation, certain microorganisms may facilitate the establishment of invasive weeds in adverse environments. This study highlights the significance of microbial functional traits in invasion ecology and suggests novel approaches for microbiome-based invasive weed management, with potential applications in agricultural soil health improvement and ecological restoration. Full article

(This article belongs to the Topic Microbe-Induced Abiotic Stress Alleviation in Plants)

17 pages, 1167 KiB

Open AccessReview

Artificial Intelligence and Its Role in Predicting Periprosthetic Joint Infections

by Diana Elena Vulpe, Catalin Anghel, Cristian Scheau, Serban Dragosloveanu and Oana Săndulescu

Biomedicines 2025, 13(8), 1855; https://doi.org/10.3390/biomedicines13081855 (registering DOI) - 30 Jul 2025

Abstract

Periprosthetic joint infections (PJIs) represent one of the most problematic complications following total joint replacement, with a significant impact on the patient’s quality of life and healthcare costs. The early and accurate diagnosis of a PJI remains the key factor in the management [...] Read more.

Periprosthetic joint infections (PJIs) represent one of the most problematic complications following total joint replacement, with a significant impact on the patient’s quality of life and healthcare costs. The early and accurate diagnosis of a PJI remains the key factor in the management of such cases. However, with traditional diagnostic measures and risk assessment tools, the early identification of a PJI may not always be adequate. Artificial intelligence (AI) algorithms have been integrated in most technological domains, with recent integration into healthcare, providing promising applications due to their capability of analyzing vast and complex datasets. With the development and implementation of AI algorithms, the assessment of risk factors and the prediction of certain complications have become more efficient. This review aims to not only provide an overview of the current use of AI in predicting PJIs, the exploration of the types of algorithms used, and the performance metrics reported, but also the limitations and challenges that come with implementing such tools in clinical practice. Full article

(This article belongs to the Special Issue Musculoskeletal Injuries: Clinical, Radiological, and Biochemical Evaluation in Orthopedics)

17 pages, 3273 KiB

Open AccessArticle

Cluster Partitioning and Reactive Power–Voltage Control Strategy for Distribution Systems with High-Penetration Distributed PV Integration

by Bingxu Zhai, Kaiyu Liu, Yuanzhuo Li, Zhilin Jiang, Panhao Qin, Wang Zhang and Yuanshi Zhang

Processes 2025, 13(8), 2423; https://doi.org/10.3390/pr13082423 (registering DOI) - 30 Jul 2025

Abstract

The large-scale integration of renewable energy into power systems poses significant challenges to reactive power and voltage stability. To enhance system stability, this work proposes a cluster partitioning and distributed control strategy for distribution networks with high-penetration distributed PV integration. Firstly, a comprehensive [...] Read more.

The large-scale integration of renewable energy into power systems poses significant challenges to reactive power and voltage stability. To enhance system stability, this work proposes a cluster partitioning and distributed control strategy for distribution networks with high-penetration distributed PV integration. Firstly, a comprehensive clustering index system, including electrical distance, voltage sensitivity, and regulation ability, is established. Considering the voltage and reactive power support capability of regional clusters, the distribution network is divided into clusters. Subsequently, based on the results of cluster division, a hierarchical partition optimization model is constructed with voltage and reactive power as the optimization objectives. Finally, a distributed optimization algorithm based on ADMM is proposed to solve the optimization model and maximize the utilization of distribution network control resources. The simulation results based on the IEEE 33-node distribution system verify the effectiveness of the proposed distributed optimization strategy. Full article

(This article belongs to the Section Energy Systems)

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18 pages, 2433 KiB

Open AccessFeature PaperArticle

Effect of Preharvest Aluminum-Coated Paper Bagging on Postharvest Quality, Storability, and Browning Behavior of ‘Afrata Volou’ Quince

by Triantafyllia Georgoudaki, Persefoni Maletsika and George D. Nanos

Horticulturae 2025, 11(8), 881; https://doi.org/10.3390/horticulturae11080881 (registering DOI) - 30 Jul 2025

Abstract

As consumer preferences tend toward safer, chemical residue-free, and nutritionally rich fruits, preharvest bagging has gained attention as a sustainable method for improving fruit quality and protecting produce from environmental and biological stressors and pesticide residues. This study assessed the impact of preharvest [...] Read more.

As consumer preferences tend toward safer, chemical residue-free, and nutritionally rich fruits, preharvest bagging has gained attention as a sustainable method for improving fruit quality and protecting produce from environmental and biological stressors and pesticide residues. This study assessed the impact of preharvest bagging using paper bags with inner aluminum coating on the physicochemical traits, storability, and browning susceptibility after cutting or bruising of ‘Afrata Volou’ quince (Cydonia oblonga Mill.) fruit grown in central Greece. Fruits were either bagged or left unbagged approximately 60 days before harvest, and evaluations were conducted at harvest and after three months of cold storage, plus two days of shelf-life. Fruit bagging reduced the quince’s flesh temperature on the tree crown. Bagging had minor effects on fruit and nutritional quality, except for more yellow skin and higher titratable acidity (TA). Also, at harvest, bagging did not significantly affect fruit flesh browning after cutting or bruising. After three months of storage, unbagged and bagged quince fruit developed more yellow skin color, without significant alterations in most quality characteristics and nutritional value, but increased total tannin content (TTC). After three months of storage, the quince flesh color determined immediately after cutting or bruising was brighter and more yellowish compared to that at harvest, due to continuation of fruit ripening, but it darkened faster with time after cutting or skin removal. Therefore, fruit bagging appears to be a sustainable practice for improving the aesthetic and some chemical quality characteristics of quince, particularly after storage, without negative impacts on other characteristics such as texture and phenolic content. Full article

(This article belongs to the Special Issue Advances in Tree Crop Cultivation and Fruit Quality Assessment)

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18 pages, 3967 KiB

Open AccessArticle

A Thorough Investigation of the Mechanism of theAntagonistic Effect Between Phosphorus and Basic Oxide-Forming Minerals as Flame Retardants of PolymericComposite Coatings

by Evangelia Mitropoulou, Georgios N. Mathioudakis, Amaia Soto Beobide, Athanasios Porfyris, Vassilios Dracopoulos, Kerim Kılınç, Theodosios Chatzinikolaou, Deniz Savci, Cem Gunesoglu, Joannis Kallitsis and George A. Voyiatzis

Coatings 2025, 15(8), 886; https://doi.org/10.3390/coatings15080886 (registering DOI) - 30 Jul 2025

Abstract

Halogenated flame retardants have been amongst the most widely used and effective solutions for enhancing fire resistance. However, their use is currently strictly regulated due to serious health and environmental concerns. In this context, phosphorus-based and mineral flame retardants have emerged as promising [...] Read more.

Halogenated flame retardants have been amongst the most widely used and effective solutions for enhancing fire resistance. However, their use is currently strictly regulated due to serious health and environmental concerns. In this context, phosphorus-based and mineral flame retardants have emerged as promising alternatives. Despite this, their combined use is neither straightforward nor guaranteed to be effective. This study scrutinizes the interactions between these two classes of flame retardants (FR) through a systematic analysis aimed at elucidating the antagonistic pathways that arise from their coexistence. Specifically, this study focuses on two inorganic fillers, mineral huntite and chemically precipitated magnesium hydroxide, both of which produce basic oxides upon thermal decomposition. These fillers were incorporated into a poly(butylene terephthalate) (PBT) matrix to be utilized as advanced-mattress FR coating fabric and were subjected to a series of flammability tests. The pyrolysis products of the prepared polymeric composite compounds were isolated and thoroughly characterized using a combination of analytical techniques. Thermogravimetric analysis (TGA) and differential thermogravimetric analysis (dTGA) were employed to monitor decomposition behavior, while the char residues collected at different pyrolysis stages were examined spectroscopically, using FTIR-ATR and Raman spectroscopy, to identify their structure and the chemical reactions that led to their formation. X-ray diffraction (XRD) experiments were also conducted to complement the spectroscopic findings in the chemical composition of the resulting char residues and to pinpoint the different species that constitute them. The morphological changes of the char’s structure were monitored by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS). Finally, the Limited Oxygen Index (LOI) and UL94 (vertical sample mode) methods were used to assess the relative flammability of the samples, revealing a significant drop in flame retardancy when both types of flame retardants are present. This reduction is attributed to the neutralization of acidic phosphorus species by the basic oxides generated during the decomposition of the basic inorganic fillers, as confirmed by the characterization techniques employed. These findings underscore the challenge of combining organophosphorus with popular flame-retardant classes such as mineral or basic metal flame retardants, offering insight into a key difficulty in formulating next-generation halogen-free flame-retardant composite coatings. Full article

(This article belongs to the Special Issue Innovative Flame-Retardant Coatings for High-Performance Materials)

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