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28 pages, 8091 KB

Open AccessArticle

Identification of Bacterial Networks and Relationship to Host Responses in Early Periodontitis Population over 24 Months

by Aaron R. Biesbrock, Sancai Xie, Ping Hu, Cheryl S. Tansky, Xingtao Wei, Hao Ye, Benjamin Circello, Avi Zini, Guy Tobias, Makio Tamura and Mirjana Parlov

Int. J. Mol. Sci. 2025, 26(22), 10823; https://doi.org/10.3390/ijms262210823 (registering DOI) - 7 Nov 2025

Abstract

This research examined the effects of daily application of an oral hygiene regimen on the subgingival microbiome over 24 months. Generally healthy adults (107 enrolled, 87 completed) with early periodontitis used a home-care regimen (stannous fluoride paste, cetylpyridinium chloride rinse, power toothbrush, and [...] Read more.

This research examined the effects of daily application of an oral hygiene regimen on the subgingival microbiome over 24 months. Generally healthy adults (107 enrolled, 87 completed) with early periodontitis used a home-care regimen (stannous fluoride paste, cetylpyridinium chloride rinse, power toothbrush, and floss) or usual care (control). Subgingival plaque samples were analyzed enzymatically for bacterial toxins. TLR ligands were measured using TLR-SEAP and TLR-ATP assays. Proinflammatory cytokines and metalloproteinases were quantified via immunoassays. Subgingival DNA was sequenced using a shotgun approach to assess microbial diversity. Increasing levels of bacteria, toxins, TLR activation, inflammatory cytokines, and MMPs were observed for periodontitis versus gingivitis and gingivitis versus healthy sites. The regimen significantly reduced levels of the critical proinflammatory cytokine IL-1β, as well as MMP-1 and MMP-9, at 24 months. By month 6, TLR ligands within subgingival plaques decreased. The abundance of pathogenic bacteria correlated with levels of virulence factors, proinflammatory cytokines, MMPs, and severity of clinical measures. Two distinct constellations of pathogenic bacteria were identified. Gingival sites were categorized into responders and non-responders per clinical symptoms and biomarkers. The regimen yielded more responder sites (70%) versus the control (47%), p = 0.0002914. The regimen reduced pathogenic bacteria, IL-1β, MMP1, and MMP-9, paralleling clinical reductions in periodontal disease. Full article

(This article belongs to the Special Issue Molecular Biology of Periodontal Disease and Periodontal Pathogens)

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10 pages, 258 KB

Open AccessArticle

In Vitro Activity of Ethanolic Extract and Essential Oil of Achyrocline satureioides Against Larvae of the Tick Rhipicephalus sanguineus

by Rafaela Regina Fantatto, Flávio Augusto Sanches Politi, Rodrigo Sorrechia and Rosemeire Cristina Linhari Rodrigues Pietro

Parasitologia 2025, 5(4), 60; https://doi.org/10.3390/parasitologia5040060 (registering DOI) - 7 Nov 2025

Abstract

The tick Rhipicephalus sanguineus is the most prevalent ectoparasite in dogs, causing discomfort to the animals and acting as a vector for several pathogens, including the bacterium Ehrlichia canis and the protozoa Babesia canis, Babesia gibsoni, and Hepatozoon canis. Control [...] Read more.

The tick Rhipicephalus sanguineus is the most prevalent ectoparasite in dogs, causing discomfort to the animals and acting as a vector for several pathogens, including the bacterium Ehrlichia canis and the protozoa Babesia canis, Babesia gibsoni, and Hepatozoon canis. Control of this parasite is traditionally carried out with synthetic chemical acaricides. However, due to the increasing number of cases of resistance, phytotherapy has been increasingly investigated as a promising alternative. In this study, the larvicidal activity of the crude ethanolic extract and essential oil obtained from the inflorescences of Achyrocline satureioides was evaluated, whose constituents were identified through phytochemical analyses and gas chromatography. The analyses revealed that the extract is rich in flavonoids, tannins, and saponins, while the essential oil is composed mainly of terpenes. In contact tests with impregnated paper, the extract at 100 mg/mL showed a mortality rate of 32.2% in R. sanguineus larvae with LC₅₀ calculated at 249.62 mg/m., while the essential oil, at the same concentration, resulted in 56.55% mortality, and the LC₅₀ and LC₉₀ were 119.73 mg/mL and 185.53 mg/mL, respectively. These results indicate that the essential oil of A. satureioides has significant larvicidal activity and has potential for use as an alternative, alone or in combination with other extracts or synthetic acaricides. Full article

(This article belongs to the Special Issue Exploring Natural Products as Antiparasitic Agents: Efficacy Against Parasites of Veterinary and Public Health Significance)

17 pages, 1904 KB

Open AccessArticle

Optimal Deployment of Low-Voltage Instrument Transformers Considering Time-Varying Risk Assessment

by Yinglong Diao, Jiawei Fan, Kangmin Hu, Lei Yang and Qiang Yao

Electronics 2025, 14(22), 4361; https://doi.org/10.3390/electronics14224361 (registering DOI) - 7 Nov 2025

Abstract

To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, [...] Read more.

To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, considering cascading faults and dynamic load fluctuations. A multi-objective optimization model minimizes deployment costs while maximizing fault coverage, incorporating dynamic response constraints. A Genetic-Greedy Hybrid Algorithm (GGHA) with intelligent initialization and elite retention mechanisms is proposed to solve the complex spatiotemporal coupling problem. Simulation results demonstrate that GGHA achieves solution quality of 0.847, outperforming PSO, GA, and GD by 7.5%, 11.7%, and 8.7%, respectively, with convergence stability within ±2.5%. The strategy maintains 100% normal coverage and 73.3–95.5% disaster coverage across flood severity levels, exhibiting strong feasibility and generalizability on IEEE 123-node and 33-node test systems. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Diagnosis, and Prognostics for Power Equipment)

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23 pages, 2369 KB

Open AccessReview

ECMO in Refractory Septic Shock: Patient Selection, Timing and Hemodynamic Targets

by Debora Emanuela Torre and Carmelo Pirri

J. Clin. Med. 2025, 14(22), 7904; https://doi.org/10.3390/jcm14227904 (registering DOI) - 7 Nov 2025

Abstract

Background: Septic shock remains a major cause of mortality in critical care, driven by profound vasoplegia, myocardial depression and refractory circulatory collapse. Conventional therapy occasionally fails to restore adequate perfusion, leading to life-threatening multi-organ failure. Methods: This narrative review examines current evidence [...] Read more.

Background: Septic shock remains a major cause of mortality in critical care, driven by profound vasoplegia, myocardial depression and refractory circulatory collapse. Conventional therapy occasionally fails to restore adequate perfusion, leading to life-threatening multi-organ failure. Methods: This narrative review examines current evidence on veno-arterial extracorporeal membrane oxygenation (V-A ECMO) as a salvage strategy for refractory septic shock, focusing on the pathophysiological rationale, patient selection, timing of initiation and hemodynamic management. Results: Data from observational studies and registry analyses suggest that V-A ECMO may improve survival in patients with septic cardiomyopathy (SCM), with reported survival rates approaching 40% in selected adult cohorts and over 50% in pediatric populations. Early initiation, phenotype-guided selection and precise hemodynamic titration are critical to optimize outcomes. Conclusions: The role of ECMO in septic shock remains controversial and should be restricted to experienced centers and well-defined phenotypes. Future research must refine selection criteria, standardize support strategies and evaluate long-term functional recovery beyond survival. Full article

(This article belongs to the Special Issue Cardiac Surgery: Clinical Advances)

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12 pages, 3183 KB

Open AccessArticle

In Vivo Quantitative Monitoring of Drug Release from Halo-Spun Rubbery Mats by Fluorescent Organism Bioimaging (FOBI)

by Peter Polyak, Aswathy Sasidharan Pillai, Laszlo Forgach, Kristof Molnar, Judit E. Puskas and Domokos Mathe

Polymers 2025, 17(22), 2972; https://doi.org/10.3390/polym17222972 (registering DOI) - 7 Nov 2025

Abstract

This paper will present in vivo release profiles of Doxorubicin.HCl from halo-spun drug-loaded rubbery porous mats. For the very first time, Fluorescent Organism Bioimaging (FOBI) was used to follow drug release in a live animal model with induced tumors. A new predictive model [...] Read more.

This paper will present in vivo release profiles of Doxorubicin.HCl from halo-spun drug-loaded rubbery porous mats. For the very first time, Fluorescent Organism Bioimaging (FOBI) was used to follow drug release in a live animal model with induced tumors. A new predictive model based on apparent diffusion coefficients to simulate release profiles will also be presented and could have general applications for release profile predictions. Surprisingly, histological evaluation found that the tissue layer forming next to the drug-eluting mats had unordered morphology and only necrotic cells. This is a stunning contrast to the highly regular collagen structure next to mats without the drug, typical of an adverse foreign body type reaction. The findings suggest that this drug-eluting fiber mat can be used as a local chemotherapy approach coupled with mitigation of capsular contracture, the major complication associated with breast reconstruction following mastectomy. Full article

(This article belongs to the Section Biobased and Biodegradable Polymers)

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24 pages, 7939 KB

Open AccessArticle

From Depletion to Recovery: Tracking Water Storage Changes in the Semiarid Region of Inner Mongolia, China

by Donghua Zhang, Junhuan Peng, Fengwei Wang, Tengfei Feng, Yanan Tian, Ruizhong Gao and Long Ma

Remote Sens. 2025, 17(22), 3668; https://doi.org/10.3390/rs17223668 (registering DOI) - 7 Nov 2025

Abstract

Inner Mongolia is an important energy producer and the sixth-largest grain-supplying region in China. To address crucial water security challenges, the spatiotemporal variations in terrestrial water storage (TWS) and groundwater storage (GWS) in semiarid Inner Mongolia from April 2002 to January 2025 were [...] Read more.

Inner Mongolia is an important energy producer and the sixth-largest grain-supplying region in China. To address crucial water security challenges, the spatiotemporal variations in terrestrial water storage (TWS) and groundwater storage (GWS) in semiarid Inner Mongolia from April 2002 to January 2025 were evaluated on the basis of the synergistic use of multisource data, including satellite gravimetry, hydrological models, and meteorological data. There was a loss of TWS in Inner Mongolia (−1.69 ± 0.17 mm/year), which was caused mainly by the depletion of groundwater (−4.90 ± 0.12 mm/year), and it offset a slight increase in surface water (+3.21 ± 0.19 mm/year). Marked declines were clustered mainly in the central/southern regions (e.g., Ordos: GWS of −10.20 ± 0.19 mm/year), whereas the northeastern region (e.g., Hulun Buir) experienced an increase (+5.09 mm/year), which was related to abundant rainfall. Notably, the declining trend of GWS across all of Inner Mongolia before 2022 (−5.49 ± 0.17 mm/year) achieved an unprecedented reversal after 2022 (+17.80 ± 0.21 mm/year), indicating the significant influence of policy interventions and precipitation changes. In the central/eastern agro-pastoral zones, water loss was driven mainly by human-related activities such as coal mining and farming; in contrast, aridity in the west was worsened by climate variability. Therefore, it is crucial to formulate urgent water redistribution strategies, promote efficient irrigation methods, and improve monitoring systems for the purpose of protecting energy and food security and strengthening ecological adaptability in the context of climate change. Full article

(This article belongs to the Special Issue Space-Geodetic Techniques (Third Edition))

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29 pages, 2460 KB

Open AccessArticle

Can Biodiversity Disclosure Improve Stock Liquidity? Evidence from China

by Haonan Lin, Yongliang Yang and Mengmeng Qiang

Sustainability 2025, 17(22), 9950; https://doi.org/10.3390/su17229950 (registering DOI) - 7 Nov 2025

Abstract

Biodiversity loss poses a threat to corporate performance and social welfare. Biodiversity disclosure enables investors to evaluate firms’ biodiversity status. However, it remains unclear whether and how biodiversity disclosure affects capital market efficiency. In this paper, we employ a binary variable derived from [...] Read more.

Biodiversity loss poses a threat to corporate performance and social welfare. Biodiversity disclosure enables investors to evaluate firms’ biodiversity status. However, it remains unclear whether and how biodiversity disclosure affects capital market efficiency. In this paper, we employ a binary variable derived from a word-frequency analysis of annual reports to determine whether a firm has disclosed biodiversity information. Using a panel of Chinese listed companies from 2011 to 2022, we provide robust evidence that Companies that disclose biodiversity information have experienced sustained improvements in stock liquidity. Furthermore, the effect is significantly amplified after the 2020 UN Biodiversity Summit, suggesting that investors respond positively to biodiversity disclosure. Channel analysis reveals that higher inventory turnover reinforces this positive effect, while greater financing constraints and higher management ownership weaken it. Heterogeneity analysis further indicates that this effect is more pronounced among firms with higher environmental information asymmetry, lower supply chain transparency, and lower patient capital. This study sheds light on how biodiversity disclosure affects market efficiency and offers important insights for future research and policy. Full article

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30 pages, 27621 KB

Open AccessArticle

A Robust Corroded Metal Fitting Detection Approach for UAV Intelligent Inspection with Knowledge-Distilled Lightweight YOLO Model

by Yangyang Tian, Weijian Zhang, Zhe Li, Junfei Liu and Wentao Mao

Electronics 2025, 14(22), 4362; https://doi.org/10.3390/electronics14224362 (registering DOI) - 7 Nov 2025

Abstract

Detecting corroded metal fittings in UAV-based transmission line inspections is challenging due to the small object size and environmental interference, causing high false and missed detection rates. To address these, this paper proposes a novel knowledge-distilled lightweight YOLO model, integrating a densely-connected convolutional [...] Read more.

Detecting corroded metal fittings in UAV-based transmission line inspections is challenging due to the small object size and environmental interference, causing high false and missed detection rates. To address these, this paper proposes a novel knowledge-distilled lightweight YOLO model, integrating a densely-connected convolutional network and spatial pixel-aware self-attention mechanism in the teacher model training stage to enhance feature transfer and structured feature utilization for reducing environmental interference, while employing the lightweight MobileNet as the feature extractor in the student model training stage and optimizing candidate box migration via the teacher model’s efficient intersection-over-union non-maximum suppression (EIoU-NMS). This model overcomes the challenges of small-object fitting detection in complex environments, improving fault identification accuracy and reducing manual inspection costs and missed detection risks, while its lightweight design enables rapid deployment and real-time detection on UAV terminals, providing a reliable technical solution for unmanned smart grid operation. Experimental results on actual UAV inspection images demonstrate that the model significantly enhances detection accuracy, reduces false and missed detections, and achieves faster speeds with substantially fewer parameters, highlighting its outstanding effectiveness and practicality in power system maintenance scenarios. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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30 pages, 6439 KB

Open AccessArticle

Three-Dimensional Numerical Analyses of a Monitored Deep Excavation Pit: A Case Study in the Guangzhou Metro

by Wentian Xu, Lifen Lin, Nengwen Zhu, Yan Zhao, Hong Yang, Yuan Mei and Dongbo Zhou

Buildings 2025, 15(22), 4018; https://doi.org/10.3390/buildings15224018 (registering DOI) - 7 Nov 2025

Abstract

This paper focuses on a deep foundation pit project of a metro shaft constructed by the cover-and-excavation reverse method in a section of Guangzhou Metro Line 13. This study integrates field monitoring data, three-dimensional finite element simulations, and parametric analyses to propose a [...] Read more.

This paper focuses on a deep foundation pit project of a metro shaft constructed by the cover-and-excavation reverse method in a section of Guangzhou Metro Line 13. This study integrates field monitoring data, three-dimensional finite element simulations, and parametric analyses to propose a systematic optimization design framework, providing a more comprehensive and reliable quantitative basis for the design of support structures for deep metro foundation pits constructed using the cut-and-cover top-down method. The study examines the effects of pile diameter, pile spacing, embedment depth, and types of retaining structures on pit deformation. The results indicate that increasing the pile diameter from 800 mm to 1000 mm reduces the maximum lateral displacement of the retaining structure by 30.7%, while decreasing the pile spacing from 2000 mm to 1600 mm results in a 17.5% reduction in deformation. However, beyond these thresholds, the marginal improvement becomes less significant. An embedment depth of 4 m for shallow sections and 2.5 m for deep sections is recommended to balance deformation control and construction economy. Diaphragm walls outperform bored piles and secant piles in deformation control. The optimized design achieves an estimated cost reduction of approximately 15% while maintaining safety requirements. The optimized parameters and comparative conclusions derived from this study can be directly applied to the design of deep foundation pits for metro stations under similar geological conditions. These findings provide crucial data support and theoretical reference for formulating more economical and safer design codes and standards. Full article

(This article belongs to the Special Issue Advancing Civil Engineering Construction and Management: Innovations in Green Building, Intelligent Construction, and Sustainable Infrastructure Development)

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12 pages, 200 KB

Open AccessArticle

Clinical Practice of Nursing Students in South Korea’s Community Treatment Centers During COVID-19: A Descriptive Phenomenological Study

by Yungyong Jeon, Chung-uk Oh, Misook Park, Seunyoung Joe and Eunji Kwon

Healthcare 2025, 13(22), 2829; https://doi.org/10.3390/healthcare13222829 (registering DOI) - 7 Nov 2025

Abstract

Background/Objectives: This study explored the lived experiences of nursing students in South Korea who participated in clinical practice at Community Treatment Centers (CTCs) during the COVID-19 pandemic. Methods: This study was designed as a qualitative study and applied Colaizzi’s descriptive phenomenology. [...] Read more.

Background/Objectives: This study explored the lived experiences of nursing students in South Korea who participated in clinical practice at Community Treatment Centers (CTCs) during the COVID-19 pandemic. Methods: This study was designed as a qualitative study and applied Colaizzi’s descriptive phenomenology. Semi-structured interviews were conducted with ten nursing students who practiced at CTCs for three to four weeks. Data were analyzed through Colaizzi’s seven procedural steps to derive the essential structure of their experience. Data saturation was achieved, and methodological rigor criteria were applied. Results: Four overarching themes emerged: (1) transformative growth through immersive clinical practice in quarantine; (2) enduring and adapting to uncertainty and emotional turmoil; (3) reconciling vulnerability and responsibility as future professionals; and (4) validation and pride in becoming visible during a national crisis. Conclusions: The study revealed that CTC practice constituted a transformative learning experience that enhanced students’ professional identity and resilience in disaster situations. Findings highlight the need to integrate disaster ethics and psychosocial preparedness into undergraduate nursing curricula. Full article

(This article belongs to the Section Healthcare in Epidemics and Pandemics)

16 pages, 3701 KB

Open AccessArticle

Early Osseointegration in a Sheep Tibia Model: Correlating Digital Periapical Radiograph Gray-Level and RGB-Derived Metrics with Histologic Tissue Composition

by Sergio Alexandre Gehrke, Jaime Aramburú Júnior, Tiago Luis Eilers Treichel, Germán Odella Colla, Gustavo Coura, Bruno Freitas Mello, Márcio de Carvalho Formiga, Fátima de Campos Buzzi, Sergio Rexhep Tari and Antonio Scarano

J. Funct. Biomater. 2025, 16(11), 415; https://doi.org/10.3390/jfb16110415 (registering DOI) - 7 Nov 2025

Abstract

Objective: This study aimed to evaluate peri-implant tissue changes during early osseointegration using a combined approach of digital radiographic analysis, RGB pseudocolorization, and histomorphometry in a sheep tibia model. Materials and Methods: Thirty titanium implants were placed in the tibiae of six adult [...] Read more.

Objective: This study aimed to evaluate peri-implant tissue changes during early osseointegration using a combined approach of digital radiographic analysis, RGB pseudocolorization, and histomorphometry in a sheep tibia model. Materials and Methods: Thirty titanium implants were placed in the tibiae of six adult sheep and evaluated at 14 and 28 days post-implantation. Digital periapical radiographs were acquired, grayscale values and RGB channel intensities were measured using Fiji/ImageJ, and compared with histological parameters (bone tissue, collagen, and medullary spaces) quantified from picrosirius–hematoxylin-stained sections. Manual overlay of radiographic and histological images was performed to ensure spatial correspondence of regions of interest. Statistical analyses assessed differences over time and correlations between image data and histological composition. Results: Radiographic grayscale values and histologically measured bone and collagen increased significantly from 14 to 28 days (p < 0.01), while medullary spaces decreased (p < 0.001), indicating progressive bone formation and matrix maturation. RGB analysis revealed significant increases in green channel intensity and decreases in red channel intensity (p < 0.05), while the blue channel remained stable. At 14 days, strong correlations were observed between blue channel intensity and bone tissue (r = 0.81; p = 0.015), and between green channel intensity and collagen (r = 0.98; p < 0.001). Visual overlays demonstrated alignment between radiographic high-density zones and histologically dense bone regions. Conclusions: RGB pseudocolorized radiographic analysis, correlated with histological findings, offers a non-invasive and reproducible method for early detection of peri-implant tissue maturation. This feasibility correlation study provides a foundation for future investigations integrating imaging, histology, and biomechanical testing. Full article

(This article belongs to the Special Issue Functional Biomaterial for Bone Regeneration (2nd Edition))

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29 pages, 1890 KB

Open AccessArticle

Impact of Mining on Socioeconomic Status in Puno, Peru

by Rene Paz Paredes, Roberto Arpi, Oliver Amadeo Vilca Huayta, Roberto Chavez Flores, Henry Sucari Turpo, Roberto Alfaro-Alejo, Alcides Huamani and Hernan Saravia

Sustainability 2025, 17(22), 9951; https://doi.org/10.3390/su17229951 (registering DOI) - 7 Nov 2025

Abstract

This study examines the direct and indirect effects of mining activities on key socioeconomic indicators such as per capita income, the Human Development Index (HDI), and education in the Puno region of Peru, comparing short-term (2015–2019) and long-term (2003–2029) impacts. Using a random [...] Read more.

This study examines the direct and indirect effects of mining activities on key socioeconomic indicators such as per capita income, the Human Development Index (HDI), and education in the Puno region of Peru, comparing short-term (2015–2019) and long-term (2003–2029) impacts. Using a random effects panel data model and incorporating spatial autocorrelation, the study analyzes data from 2003 to 2019 to assess the effects of mining on both mining and non-mining districts. The results show that in the short term, family income per capita in mining districts increased by PEN 65.03, while non-mining districts saw an indirect increase of PEN 80.59. In the long term, the direct impact on mining districts grew to PEN 239.44, with the indirect impact on non-mining districts reaching PEN 352.30. In education, mining districts experienced a 6.74 percentage point increase in secondary education for 18-year-olds in the short term, and non-mining districts had a 5.19 percentage point increase, with both showing positive impacts. Long-term effects showed a smaller increase, with mining districts at 12.27 percentage points and non-mining districts at 9.71 percentage points. Regarding the HDI, the direct impact in mining districts in the short term was an increase of 0.02 points, with a total impact of 0.03 points, while the indirect impact on non-mining districts was minimal. In the long term, the direct impact on mining districts grew to 0.09 points, with the total impact reaching 0.10 points, while non-mining districts showed an increase of 0.10 points as well. The study concludes that mining has significant short-term impacts, particularly on income and education, but the long-term effects are more pronounced, especially for income and the HDI, with substantial indirect benefits for non-mining districts, especially in terms of income. Educational improvements stabilize over time, and mining’s overall impact on the HDI increases as its economic and social effects deepen. Full article

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14 pages, 1884 KB

Open AccessArticle

Effects of Foliar Application of Paclobutrazol on Grain Yield, Aroma, and Canopy Radiation Use Efficiency of Aromatic Rice

by Fengqin Hu, Jian Lu, Laiyuan Zhai, Xianjin Qiu, Bin Du and Jianlong Xu

Biology 2025, 14(11), 1562; https://doi.org/10.3390/biology14111562 (registering DOI) - 7 Nov 2025

Abstract

Paclobutrazol (PBZ) is extensively used to modulate plant architecture in rice. However, its comprehensive effects on grain yield and aroma in aromatic rice have not been thoroughly investigated. This study used the local aromatic rice cultivars (Meixiangzhan 2 and Xiangyaxiangzhan) as experimental materials [...] Read more.

Paclobutrazol (PBZ) is extensively used to modulate plant architecture in rice. However, its comprehensive effects on grain yield and aroma in aromatic rice have not been thoroughly investigated. This study used the local aromatic rice cultivars (Meixiangzhan 2 and Xiangyaxiangzhan) as experimental materials to evaluate the impacts of foliar-applied PBZ at three concentrations (0 (CK), 150 (T1), and 300 (T2) mg L⁻¹) on grain yield, photosynthetic characteristics, fragrance formation, and radiation use efficiency (RUE). Field experiments revealed that T1 significantly reduced the leaf area index (LAI) by 10.12% and intercepted photosynthetically active radiation (IPAR) by 10.74%, meanwhile significantly increasing SPAD values by 12.94% and net photosynthetic rate (Pn) by 9.95%, leading to improved RUE up to 25.21%. These changes contributed to a larger number of grains per panicle and increased 1000-grain weight, ultimately enhancing grain yield. In contrast, T2 resulted in a sharp reduction by 24.84% in IPAR and a significant decline in Pn by 10.07% during the late grain-filling stage, thus limiting the supply of photosynthetic assimilates, eventually reducing grain yield. PBZ application also significantly elevated 2-acetyl-1-pyrroline (2-AP) content by 28.74% under T1 and 17.51% under T2, compared to the control. The increase in 2-AP was mainly associated with elevated levels of key precursors, including proline, Δ1-pyrroline-5-carboxylic acid, and Δ1-pyrroline. In spite of differences in traits between cultivars, the traits responded to PBZ in the same pattern. These results indicate that foliar application of PBZ at 150 mg L⁻¹ can effectively improve both yield and aroma of aromatic rice, offering a promising cultivation strategy for high-quality aromatic rice production. Full article

(This article belongs to the Section Plant Science)

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17 pages, 1728 KB

Open AccessArticle

Multi-Criteria-Based Key Transmission Section Identification and Prevention–Emergency Coordinated Optimal Control Strategy

by Xinyu Peng, Chuan He, Honghao Zhang, Lu Nan, Tianqi Liu, Jian Gao, Biao Wang, Xi Ye and Xinwei Sun

Energies 2025, 18(22), 5871; https://doi.org/10.3390/en18225871 (registering DOI) - 7 Nov 2025

Abstract

Large-scale blackouts in power systems are often triggered by weak links susceptible to cascading failures. As the concentrated reflection of the system’s weak links, identifying key transmission sections and further implementing safety control measures are of great significance for ensuring the stable operation [...] Read more.

Large-scale blackouts in power systems are often triggered by weak links susceptible to cascading failures. As the concentrated reflection of the system’s weak links, identifying key transmission sections and further implementing safety control measures are of great significance for ensuring the stable operation of the system. This paper proposes a multi-criteria-based method for identifying key transmission sections and an optimal strategy for the prevention–emergency coordinated control of key transmission sections. Firstly, a line criticality index based on three characteristics—topology, power flow, and voltage—has been established to identify critical lines. Furthermore, search for all initial transmission sections that include the critical line, and form the initial transmission section set for each critical line, then, based on the analysis of the Theil index of power flow impact rate distribution after the failure of critical lines, a key transmission section identification method integrating multiple criteria is proposed. Then, based on the anticipated faults of key transmission sections, an optimization model for the prevention–emergency coordinated control of key transmission sections is established. A constraint relaxation factor is introduced to divide the above model into two independent sub-problems, then the golden section method is applied to update the value of constraint relaxation factors, so as to iteratively search for the optimal solution of the model. Finally, the feasibility and correctness of the proposed method are verified through the simulation and analysis of the IEEE 39-bus system. The results demonstrate that the proposed method can effectively identify the key transmission sections of the system and improve the operational safety of the system through the prevention–emergency coordinated optimal control strategy. Full article

(This article belongs to the Special Issue Operation Optimization and Security Analysis of Energy Cyber Physical Systems—2nd Edition)

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21 pages, 6966 KB

Open AccessArticle

ACI-GNN: Lightweight All-Channel Interaction Graph Neural Network for Multi-Sensor Coal-Rock Cutting Recognition

by Zhixin Jin, Jie Cheng, Wenyan Cao, Hongwei Wang, Jiaxin Zhang, Zeping Liu, Haoran Wang and Jianzhong Li

Sensors 2025, 25(22), 6820; https://doi.org/10.3390/s25226820 (registering DOI) - 7 Nov 2025

Abstract

To address the current challenges of low single-sensor recognition accuracy for coal and rock cutting states, redundant channel feature responses, and poor performance in traditional neural network models, this paper proposes a new multi-sensor coal and rock cutting state recognition model based on [...] Read more.

To address the current challenges of low single-sensor recognition accuracy for coal and rock cutting states, redundant channel feature responses, and poor performance in traditional neural network models, this paper proposes a new multi-sensor coal and rock cutting state recognition model based on a graph neural network (GNN). This model, consisting of a feature encoder, an information exchange module, and a feature decoder, enhances the communication of feature responses between filters within the same layer, thereby improving feature capture and reducing channel redundancy. Comparative, ablation, and noise-resistance experiments on multi-sensor datasets validate the effectiveness, versatility, and robustness of the proposed model. Experimental results show that compared to the baseline models, CNN3, ResNet, and DenseNet achieve improvements of 2.47%, 2.78%, and 1.50%, respectively. With the addition of the ACI block, the ResNet model achieves the best noise-resistance performance, achieving an accuracy of 93.27% even in 6 dB noise, demonstrating excellent robustness. Embedded deployment experiments further confirmed that the proposed model maintains an inference time of less than 216.1 ms/window on the NVIDIA Jetson Nano, meeting the real-time requirements of actual industrial scenarios and demonstrating its broad application prospects in resource-constrained underground working environments. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 1841 KB

Open AccessArticle

Stochastic Game-Based Anti-Jamming Control Method for Heavy-Haul Train Group Operation

by Lin Rong, Shuomei Ma, Hongwei Wang, Taiyuan Gong, Yang Li, Xiaozhi Qi and Mingxi Ji

Electronics 2025, 14(22), 4360; https://doi.org/10.3390/electronics14224360 (registering DOI) - 7 Nov 2025

Abstract

With the growing global demand for mineral resources, enhancing the transport capacity of heavy-haul railways (HHR) has emerged as a key area of research. As an emerging train formation technology, the virtual coupling train system (VCTS) has the potential to substantially increase the [...] Read more.

With the growing global demand for mineral resources, enhancing the transport capacity of heavy-haul railways (HHR) has emerged as a key area of research. As an emerging train formation technology, the virtual coupling train system (VCTS) has the potential to substantially increase the traffic density of heavy-haul trains (HHT) and thereby improve transport efficiency. However, the stable operation of virtually coupled fleets relies on train-to-train (T2T) communication, which is vulnerable to jamming attacks (JAs) within the complex operational environments of HHR. To address issues such as train decoupling and emergency braking in the VCTS that may be caused by JAs, this study proposes a stochastic game-based anti-jamming control (SGAC) strategy aimed at ensuring the stability and operational safety of the VCTS operating within HHR. The proposed approach models both JAs and defensive actions as a stochastic game and employs an

H^{\infty}

-based cross-layer control method to mitigate their adverse effects. The control performance is analyzed through frequency-domain mapping, and a quantitative evaluation is conducted using the

H^{\infty}

norm. The simulation results demonstrate that the SGAC scheme significantly enhances the resilience of VCTS cooperative control under JAs, offering a robust solution for ensuring the stable operation of HHR. Full article

(This article belongs to the Special Issue Advancements in Autonomous Driving and Smart Transportation Systems)

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10 pages, 1462 KB

Open AccessArticle

Evaluation of the Potential Use of Four Skull Traits for Sex Estimation

by Joe Adserias-Garriga, Heli Maijanen and Sara C. Zapico

Forensic Sci. 2025, 5(4), 60; https://doi.org/10.3390/forensicsci5040060 (registering DOI) - 7 Nov 2025

Abstract

Background: Sex estimation is a basic step of human identification in both legal cases and archeological contexts. The highest accuracy for sex estimation is achieved when a complete skeleton is available, though there are situations, such as cremated, dismembered, and otherwise taphonomically [...] Read more.

Background: Sex estimation is a basic step of human identification in both legal cases and archeological contexts. The highest accuracy for sex estimation is achieved when a complete skeleton is available, though there are situations, such as cremated, dismembered, and otherwise taphonomically altered skeletal remains, where a complete skeleton is not available. The aim of the present preliminary study was to evaluate the usefulness of four non-metric skull traits that are considered taphonomically resilient for sex estimation and their potential application in forensic cases. Methods: Non-metric skull traits of 100 skulls from the Bass Donated Skeletal Collection were analyzed. These traits included foramen magnum shape, zygomatic arch extension with respect to the external auditory canal, sigmoid notch, and gonial angle muscle attachment. A discriminant function analysis model was used to develop specific formulae for sex estimation. Results: The foramen magnum and sigmoid notch showed no significant differences between males and females. The zygomatic arch extension (ZAE) and gonial angle morphology (GO) showed strong, significant differences between the sexes. However, gonial angle morphology has shown to be affected by edentulism. Based on the ZAE, the function obtained by the discriminant function analysis was sex = 2.469*ZAE − 1.247, with a result of zero pointing to males and result of one pointing to females, which correctly classified 79.8% of the original cases. Conclusions: This study highlights the value of four different skull traits and their potential use in forensic cases. Of all the evaluated traits, zygomatic arch extension was the best indicator for sex estimation. This anatomical region corresponds to a highly resistant skeletal structure. Full article

(This article belongs to the Special Issue Feature Papers in Forensic Sciences)

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13 pages, 375 KB

Open AccessArticle

Predicting Outcome and Duration of Mechanical Ventilation in Acute Hypoxemic Respiratory Failure: The PREMIER Study

by Jesús Villar, Jesús M. González-Martín, Cristina Fernández, Juan A. Soler, Marta Rey-Abalo, Juan M. Mora-Ordóñez, Ramón Ortiz-Díaz-Miguel, Lorena Fernández, Isabel Murcia, Denis Robaglia, José M. Añón, Carlos Ferrando, Dácil Parrilla, Ana M. Dominguez-Berrot, Pilar Cobeta, Domingo Martínez, Ana Amaro-Harpigny, David Andaluz-Ojeda, M. Mar Fernández, Estrella Gómez-Bentolila, Ewout W. Steyerberg, Luigi Camporota and Tamas Szakmany Show full author list Hide full author list

J. Clin. Med. 2025, 14(22), 7903; https://doi.org/10.3390/jcm14227903 (registering DOI) - 7 Nov 2025

Abstract

Objectives: The ability of clinicians to predict prolonged mechanical ventilation (MV) in patients with acute hypoxemic respiratory failure (AHRF) is inaccurate, mainly because of the competitive risk of mortality. We aimed to assess the performance of machine learning (ML) models for the early [...] Read more.

Objectives: The ability of clinicians to predict prolonged mechanical ventilation (MV) in patients with acute hypoxemic respiratory failure (AHRF) is inaccurate, mainly because of the competitive risk of mortality. We aimed to assess the performance of machine learning (ML) models for the early prediction of prolonged MV in a large cohort of patients with AHRF. Methods: We analyzed 996 ventilated AHRF patients with complete data at 48 h after diagnosis of AHRF from 1241 patients enrolled in a prospective, national epidemiological study, after excluding 245 patients ventilated for <2 days. To account for competing mortality, we used multinomial regression analysis (MNR) to model prolonged MV in three categories: (i) ICU survivors (regardless of MV duration), (ii) non-survivors ventilated for 2–7 days, (iii) non-survivors ventilated for >7 days. We performed 4 × 10-fold cross-validation to validate the performance of potent ML techniques [Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF)] for predicting patient assignment. Results: All-cause ICU mortality was 32.8% (327/996). We identified 12 key predictors at 48 h of AHRF diagnosis: age, specific comorbidities, sequential organ failure assessment score, tidal volume, PEEP, plateau pressure, PaO₂, pH, and number of organ failures. MLP showed the best predictive performance [AUC 0.86 (95%CI: 0.80–0.92) and 0.87 (0.80–0.93)], followed by MNR [AUC 0.83 (0.76–0.90) and 0.84 (0.77–0.91)], in distinguishing ICU survivors, with non-survivors ventilated 2–7 days and >7 days, respectively. Conclusions: Accounting for ICU mortality, MLP and MNR offered accurate patient-level predictions. Further work should integrate clinical and organizational factors to improve timely management and optimize outcomes. This study was initially registered on 3 February 2025 at ClinicalTrials.gov (NCT06815523). Full article

(This article belongs to the Special Issue Acute Hypoxemic Respiratory Failure: Progress, Challenges and Future)

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20 pages, 1180 KB

Open AccessSystematic Review

A Network-Based Quantitative Analysis of the Societal Impacts of Assistive Technology

by Paulo Alexandre Correia de Jesus, Jordam Wilson Lourenço, Osiris Canciglieri Junior, Ismael Cristofer Baierle and Jones Luís Schaefer

Technologies 2025, 13(11), 506; https://doi.org/10.3390/technologies13110506 (registering DOI) - 7 Nov 2025

Abstract

It is estimated that around 1.3 billion people, roughly 16% of the global population, live with some form of disability, which can be physical, auditory, visual, intellectual, or psychosocial (mental). To help this group overcome daily functional limitations and improve their ability to [...] Read more.

It is estimated that around 1.3 billion people, roughly 16% of the global population, live with some form of disability, which can be physical, auditory, visual, intellectual, or psychosocial (mental). To help this group overcome daily functional limitations and improve their ability to perform activities independently, Assistive Technologies (AT) are used. However, understanding the complex effects of these technologies on users’ lives poses challenges in measurement. This research aims to identify and systematise the impacts caused by AT within society, analysing the relationships among these impacts to offer a comprehensive understanding of their scope. A Systematic Literature Review (SLR) was carried out following the PRISMA protocol, supplemented by association rule analysis using the Apriori algorithm with Weka software. Metrics such as Support, Confidence, and Lift were used to evaluate the associations identified by the algorithm. This analysis revealed fourteen distinct types of impacts, categorised into three groups: User Quality of Life, Social and Psychosocial, and Work Environment and Productivity. The findings demonstrated consistent associations, including Autonomy → Independence, Socioeconomic Status → Social Impact, and Education → Social Impact, indicating interconnected effects of assistive devices across functional, educational, emotional, social, economic, and productivity areas. This study supports the Sustainable Development Goals by promoting the development of AT standardisation tools, guiding more inclusive public policies, and encouraging collaborative networks among stakeholders involved in AT research and development. Full article

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

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16 pages, 1593 KB

Open AccessArticle

Construction and Evaluation of a Statistical Model for a Probit Method Simulator in Pharmacological Education

by Toshiaki Ara, Hiroyuki Kitamura, Yu-Chi Hung and Kei-ichi Uchida

Appl. Biosci. 2025, 4(4), 50; https://doi.org/10.3390/applbiosci4040050 (registering DOI) - 7 Nov 2025

Abstract

Purpose: As animal welfare becomes increasingly important, there is a corresponding desire to reduce the number of animals used in experiments. Recently, we reported on statistical models for a local anaesthetic simulator and developed a simulator for use in pharmacology education. In this [...] Read more.

Purpose: As animal welfare becomes increasingly important, there is a corresponding desire to reduce the number of animals used in experiments. Recently, we reported on statistical models for a local anaesthetic simulator and developed a simulator for use in pharmacology education. In this study, we aimed to create a simulator for bioassay. Methods: Mice were intraperitoneally injected with a set concentration of lidocaine, and the time to the onset of convulsions or death was measured. Judgment times were set at 10 s intervals from 3 to 10 min. Parameter values were estimated by probit analysis based on the presence or absence of a reaction at each judgment time. The distributions and 95% confidence intervals (CI) of the estimated parameter values were confirmed using a nonparametric bootstrap method. Additionally, the generalization performance of the statistical model was confirmed using a five-fold cross-validation method. Monte Carlo simulations were performed using the estimated parameters from this model, and the average and distribution of the toxic dose 50% (TD₅₀) and lethal dose 50% (LD₅₀) were compared to those obtained from the animal experiments. Results: The parameters were properly estimated at each judgment time, and their 95% CIs were relatively narrow. The TD₅₀ and LD₅₀ values were similar across the five folds. Monte Carlo simulations demonstrated that the average and distribution of TD₅₀ and LD₅₀ were comparable to those obtained from animal experiments. Conclusions: These results suggest that a simulator based on this model is useful as an alternative to animal experiments. Therefore, our strategy will further reduce the number of experimental animals. Moreover, the method used in this study can be applied to other experiments that measure reaction time from treatment. Full article

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26 pages, 10083 KB

Open AccessArticle

Triple-Stream Contrastive Deep Embedding Clustering via Semantic Structure

by Aiyu Zheng, Jianghui Cai, Haifeng Yang, Yalin Xun and Xujun Zhao

Mathematics 2025, 13(22), 3578; https://doi.org/10.3390/math13223578 (registering DOI) - 7 Nov 2025

Abstract

Deep neural network-based deep clustering has achieved remarkable success by unifying representation learning and clustering. However, conventional representation modules are typically not tailored for clustering, resulting in conflicting objectives that hinder the model’s ability to capture semantic structures with high intra-cluster cohesion and [...] Read more.

Deep neural network-based deep clustering has achieved remarkable success by unifying representation learning and clustering. However, conventional representation modules are typically not tailored for clustering, resulting in conflicting objectives that hinder the model’s ability to capture semantic structures with high intra-cluster cohesion and low inter-cluster separation. To overcome this limitation, we propose a novel framework called Triple-stream Contrastive Deep Embedding Clustering via Semantic Structure (TCSS). TCSS is composed of representation and clustering modules, with its innovation rooted in several key designs that ensure their synergistic interaction for modeling semantic structures. First, TCSS introduces a triple-stream input framework that processes the raw instance along with its limited and aggressive augmented views. This design enables a new triple-stream self-training clustering loss, which uncovers implicit cluster structures by contrasting the three input streams. Second, within this loss, a dynamic clustering structure factor is developed to represent the evolving semantic structure in the representation space, thereby constraining the clustering-prediction distribution. Third, TCSS integrates semantic structure-aware techniques, including a clustering-oriented negative sampling strategy and a triple-stream alignment scheme based on k-nearest neighbors and centroids, to refine semantic structures both locally and globally. Extensive experiments on five benchmark datasets demonstrate that TCSS outperforms state-of-the-art methods. Full article

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23 pages, 59318 KB

Open AccessArticle

BAT-Net: Bidirectional Attention Transformer Network for Joint Single-Image Desnowing and Snow Mask Prediction

by Yongheng Zhang

Information 2025, 16(11), 966; https://doi.org/10.3390/info16110966 (registering DOI) - 7 Nov 2025

Abstract

In the wild, snow is not merely additive noise; it is a non-stationary, semi-transparent veil whose spatial statistics vary with depth, illumination, and wind. Because conventional two-stage pipelines first detect a binary mask and then inpaint the occluded regions, any early mis-classification is [...] Read more.

In the wild, snow is not merely additive noise; it is a non-stationary, semi-transparent veil whose spatial statistics vary with depth, illumination, and wind. Because conventional two-stage pipelines first detect a binary mask and then inpaint the occluded regions, any early mis-classification is irreversibly baked into the final result, leading to over-smoothed textures or ghosting artifacts. We propose BAT-Net, a Bidirectional Attention Transformer Network that frames desnowing as a coupled representation learning problem, jointly disentangling snow appearance and scene radiance in a single forward pass. Our core contributions are as follows: (1) A novel dual-decoder architecture where a background decoder and a snow decoder are coupled via a Bidirectional Attention Module (BAM). The BAM implements a continuous predict–verify–correct mechanism, allowing the background branch to dynamically accept, reject, or refine the snow branch’s occlusion hypotheses, dramatically reducing error accumulation. (2) A lightweight yet effective multi-scale feature fusion scheme comprising a Scale Conversion Module (SCM) and a Feature Aggregation Module (FAM), enabling the model to handle the large scale variance among snowflakes without a prohibitive computational cost. (3) The introduction of the FallingSnow dataset, curated to eliminate the label noise caused by irremovable ground snow in existing benchmarks, providing a cleaner benchmark for evaluating dynamic snow removal. Extensive experiments on synthetic and real-world datasets demonstrate that BAT-Net sets a new state of the art. It achieves a PSNR of 35.78 dB on the CSD dataset, outperforming the best prior model by 1.37 dB, and also achieves top results on SRRS (32.13 dB) and Snow100K (34.62 dB) datasets. The proposed method has significant practical applications in autonomous driving and surveillance systems, where accurate snow removal is crucial for maintaining visual clarity. Full article

(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning, 2nd Edition)

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17 pages, 7176 KB

Open AccessArticle

Optimizing Wastewater Treatment Reactor Design Using Computational Fluid Dynamics: Impact of Geometrical Parameters on Residence Time and Pollutant Degradation

by Bálint Levente Tarcsay, Janka Kincses, László Balogh, András Kámán, Lajos Nagy and Attila Egedy

ChemEngineering 2025, 9(6), 124; https://doi.org/10.3390/chemengineering9060124 - 7 Nov 2025

Abstract

This study investigates the impact of equipment geometry on residence time distribution (RTD) using computational fluid dynamics (CFD) methods in a wastewater treatment tank with different configurations of static mixer elements. With growing environmental concerns, optimizing wastewater treatment processes is crucial. Proper mixing [...] Read more.

This study investigates the impact of equipment geometry on residence time distribution (RTD) using computational fluid dynamics (CFD) methods in a wastewater treatment tank with different configurations of static mixer elements. With growing environmental concerns, optimizing wastewater treatment processes is crucial. Proper mixing in these units can be achieved by optimal placement of static mixer elements such as baffle walls to create circulation zones and increase residence time of the fluid within the control volume. A CFD model of a wastewater treatment tank was developed and validated using experimental RTD data under three distinct mixer configurations.The experimentally validated model was subsequently enhanced by investigating the degradation of methylene blue (MB) during ozonation in the system. The results of the model allowed for the analysis of how tank geometry—specifically, the number and placement of baffles—affects the flow field and MB conversion. RTD was characterized using expectancy and standard deviation of residence time, revealing a link between RTD and MB degradation efficiency. Results showed that constructional parameters significantly influence residence time and mixing efficiency, with a potential 60% increase in expectancy. The model demonstrated high predictive accuracy, ranging from 75% in the worst case to nearly 90% in the best case. Full article

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29 pages, 5594 KB

Open AccessArticle

Assessing Changes in Grassland Species Distribution at the Landscape Scale Using Hyperspectral Remote Sensing

by Obumneke Ohiaeri, Carlos Portillo-Quintero and Haydee Laza

Sensors 2025, 25(22), 6821; https://doi.org/10.3390/s25226821 (registering DOI) - 7 Nov 2025

Abstract

The advancement of hyperspectral remote sensing technology has enhanced the ability to assess and characterize land cover in complex ecosystems. In this study, a linear spectral unmixing algorithm was applied to NEON hyperspectral imagery in 2018 and 2022 to quantify the fractional abundance [...] Read more.

The advancement of hyperspectral remote sensing technology has enhanced the ability to assess and characterize land cover in complex ecosystems. In this study, a linear spectral unmixing algorithm was applied to NEON hyperspectral imagery in 2018 and 2022 to quantify the fractional abundance of dominant land cover classes, namely herbaceous vegetation, mixed forbs, and bare soil, across the Marvin Klemme Experimental Rangeland in Oklahoma. UAV imagery acquired during the 2023 field campaign provided high resolution reference data for model training. The LSU results revealed a decline in herbaceous cover from 16.02 ha to 11.56 ha and an expansion of bare soil from 3.37 ha to 6.39 ha, while mixed forb cover remained relatively stable (12.38 ha to 13.82 ha). Accuracy assessment using the UAV-derived validation points yielded overall accuracy of 84% and 60% at fractional thresholds of 50% and 75%, respectively. Although statistical tests indicated no significant change in mean fractional abundance (p > 0.05), slope-based trend maps captured localized vegetation loss and regrowth patterns. These findings demonstrate the effectiveness of integrating LSU with UAV data for detecting subtle yet ecologically meaningful shifts in semi-arid grassland composition. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

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24 pages, 11668 KB

Open AccessArticle

Multiphysics Optical–Thermal and Mechanical Modeling of a CMOS-SOI-MEMS Infrared Sensor with Metasurface Absorber

by Moshe Avraham and Yael Nemirovsky

Sensors 2025, 25(22), 6819; https://doi.org/10.3390/s25226819 (registering DOI) - 7 Nov 2025

Abstract

Infrared (IR) thermal sensors on CMOS-SOI-MEMS platforms enable scalable, low-cost thermal imaging but require optimized optical, thermal, and mechanical performance. This paper presents a multiphysics modeling framework to study the integration of Metasurface absorbers into a Thermal CMOS-SOI-MEMS IR sensor. Using finite-difference time-domain [...] Read more.

Infrared (IR) thermal sensors on CMOS-SOI-MEMS platforms enable scalable, low-cost thermal imaging but require optimized optical, thermal, and mechanical performance. This paper presents a multiphysics modeling framework to study the integration of Metasurface absorbers into a Thermal CMOS-SOI-MEMS IR sensor. Using finite-difference time-domain (FDTD) simulations, we demonstrate near-unity absorption at targeted wavelengths (e.g., 4.26 µm for CO₂ sensing, 10 µm for thermal imaging) compared to conventional absorbers. The absorbed power, calculated from blackbody irradiance, drives thermal finite element analysis (FEA), confirming high thermal isolation and maximized temperature rise (ΔT) while quantifying the thermal time constant’s sensitivity to Metasurface mass. An analytical RC circuit model, validated against 3D FEA, accurately captures thermal dynamics for rapid design iterations. Mechanical modal and harmonic analyses verify structural integrity, with natural frequencies above 20 kHz, ensuring resilience against mechanical resonances and environmental vibrations. This holistic framework quantifies trade-offs between optical efficiency, thermal responsivity, and mechanical stability, providing a predictive tool for designing high-performance, uncooled IR sensors compatible with CMOS processes. Full article

(This article belongs to the Special Issue Selected Papers from the 11th International Electronic Conference on Sensors and Applications)

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39 pages, 2886 KB

Open AccessReview

Sand-Based Thermal Storage System for Human-Powered Energy Generation: A Review

by Qirui Ding, Lili Zeng, Ying Zeng, Changhui Song, Liang Lei and Weicheng Cui

Energies 2025, 18(22), 5869; https://doi.org/10.3390/en18225869 (registering DOI) - 7 Nov 2025

Abstract

Sand-based thermal energy storage systems represent a paradigm shift in sustainable energy solutions, leveraging Earth’s most abundant mineral resource through advanced nanocomposite engineering. This review examines sand-based phase change materials (PCM) systems with emphasis on integration with human-powered energy generation (HPEG). Silicon-based hierarchical [...] Read more.

Sand-based thermal energy storage systems represent a paradigm shift in sustainable energy solutions, leveraging Earth’s most abundant mineral resource through advanced nanocomposite engineering. This review examines sand-based phase change materials (PCM) systems with emphasis on integration with human-powered energy generation (HPEG). Silicon-based hierarchical pore structures provide multiscale thermal conduction pathways while achieving PCM loading capacities exceeding 90%. Carbon-based nanomaterial doping enhances thermal conductivity by up to 269%, reaching 3.1 W/m·K while maintaining phase change enthalpies above 130 J/g. This demonstrated cycling stability exceeds 1000 thermal cycles with <8% capacity degradation. Thermal energy storage costs reach ~$20 kWh⁻¹—60% lower than lithium-ion systems when normalized by usable heat capacity. Integration with triboelectric nanogenerators achieves 55% peak mechanical-to-electrical conversion efficiency for direct pathways, while thermal-buffered systems provide 8–12% end-to-end efficiency with temporal decoupling between intermittent human power input and stable electrical output. Miniaturized systems target off-grid communities, offering 5–10× cost advantages over conventional batteries for resource-constrained deployments. Levelized storage costs remain competitive despite efficiency penalties versus lithium-ion alternatives. Critical challenges, including thermal cycling degradation, energy-power density trade-offs, and environmental adaptability, are systematically analyzed. Future directions explore biomimetic multi-level pore designs, intelligent responsive systems, and distributed microgrid implementations. Full article

(This article belongs to the Special Issue Advanced Energy Storage Technologies and Applications (AESAs), 2nd Edition)

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36 pages, 17074 KB

Open AccessArticle

Heterogeneous PLC-Based Distributed Controller with Embedded Logic-Monitoring Blackbox for Real-Time Failover

by Chi Kook Ryu, Min Cheol Lee, In Ho Hong, Jun Hyuk Park, Jae Deuk Lee and Su Yeon Choi

Electronics 2025, 14(22), 4359; https://doi.org/10.3390/electronics14224359 (registering DOI) - 7 Nov 2025

Abstract

This study presents a heterogeneous PLC-based distributed controller integrating an embedded logic-monitoring blackbox for real-time failover and fault detection in industrial control environments. Industrial automation and water treatment systems heavily rely on programmable logic controllers (PLCs) for process and equipment control. However, frequent [...] Read more.

This study presents a heterogeneous PLC-based distributed controller integrating an embedded logic-monitoring blackbox for real-time failover and fault detection in industrial control environments. Industrial automation and water treatment systems heavily rely on programmable logic controllers (PLCs) for process and equipment control. However, frequent failures, transient errors, and unknown malfunctions threaten system reliability and operational continuity. To address these issues, this study proposes a heterogeneous redundancy architecture consisting of a primary PLC and a standby distributed controller equipped with a logic-monitoring blackbox. The blackbox continuously monitors the I/O logic status of the primary PLC, records abnormal behaviors such as I/O faults, and enables the standby controller’s I/O to selectively execute failover operations. Unlike conventional homogeneous redundancy, which depends on identical hardware, the proposed approach adopts a Linux-based platform, offering advantages in flexibility, cost efficiency, and elimination of vendor lock-in. Furthermore, the standby controller integrates both a ladder editor and an HMI editor, allowing for direct on-site modification and editing of faulty I/O without external tools. Experimental validation was conducted using a laboratory testbed, while durability and electromagnetic compatibility (EMC) assessments were performed by an accredited institute to verify industrial applicability. Quantitatively, the mean time between failures (MTBF) increased by 17.2%, the average switchover latency was reduced to 41 ms, and the detection probability (

g

) reached 0.986 under multi-vendor configurations. All tests were performed under controlled industrial conditions using IEC 61508-compliant PLC testbeds. The results confirm that the proposed heterogeneous redundancy method significantly enhances fault detection capability, ensures rapid failover, and improves overall operational reliability in industrial automation systems. Full article

(This article belongs to the Special Issue Advanced Condition Monitoring and Fault Analysis in Industrial Electronics)

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